Evolutionary processes

@misc{ehrilch1963the1,
    author = "Ehrilch, P. R. and Holm, R. W",
    title = "The process of evolution",
    year = "1963",
    howpublished = "New York, McGraw-Hill, 347 p",
    note = "talkorigins\_source = {true}; raw\_reference = {Ehrilch, P. R., and Holm, R. W., 1963, The process of evolution: New York, McGraw-Hill, 347 p.}"
}

@article{doi101111j155856461975tb00851x,
    author = "Felsenstein, Joseph",
    title = "THE GENETIC BASIS OF EVOLUTIONARY CHANGE",
    year = "1975",
    journal = "Evolution",
    url = "https://doi.org/10.1111/j.1558-5646.1975.tb00851.x",
    doi = "10.1111/j.1558-5646.1975.tb00851.x",
    openalex = "W1547248981"
}

It is suggested that evolution in plants may be associated with the emergence of three primary strategies, each of which may be identified by reference to a number of characteristics including morphological features, resource allocation, phenology, and response to stress. The competitive strategy prevails in productive, relatively undisturbed vegetation, the stress-tolerant strategy is associated with continuously unproductive conditions, and the ruderal strategy is characteristic of severely disturbed but potentially productive habitats. A triangular model based upon the three strategies may be reconciled with the theory of r- and K-selection, provides an insight into the processes of vegetation succession and dominance, and appears to be capable of extension to fungi and to animals.

@article{doi101086283244,
    author = "Grime, J. P.",
    title = "Evidence for the Existence of Three Primary Strategies in Plants and Its Relevance to Ecological and Evolutionary Theory",
    year = "1977",
    journal = "The American Naturalist",
    abstract = "It is suggested that evolution in plants may be associated with the emergence of three primary strategies, each of which may be identified by reference to a number of characteristics including morphological features, resource allocation, phenology, and response to stress. The competitive strategy prevails in productive, relatively undisturbed vegetation, the stress-tolerant strategy is associated with continuously unproductive conditions, and the ruderal strategy is characteristic of severely disturbed but potentially productive habitats. A triangular model based upon the three strategies may be reconciled with the theory of r- and K-selection, provides an insight into the processes of vegetation succession and dominance, and appears to be capable of extension to fungi and to animals.",
    url = "https://doi.org/10.1086/283244",
    doi = "10.1086/283244",
    openalex = "W2055424972",
    references = "doi101038242344a0, doi101038250026a0, doi101086282454, doi101086282455, doi1015159781400881376, doi102307213332, doi1023072258728, doi10230725528056, doi1023073241344, doi105962bhltitle59991"
}

I. OVERVIEW AND MOTIVATION 1. Introduction 2. The Need for an Evolutionary Theory II. ORGANIZATION-THEORETIC FOUNDATIONS OF ECONOMIC EVOLUTIONARY THEORY 3. The Foundations of Contemporary Orthodoxy 4. Skills 5. Organizational Capabilities and Behavior III. TEXTBOOK ECONOMICS REVISITED 6. Static Selection Equilibrium 7. Firm and Industry Response to Changed Market Conditions IV. GROWTH THEORY 8. Neoclassical Growth Theory: A Critique 9. An Evolutionary Model of Economic Growth 10. Economic Growth as a Pure Selection Process 11. Further Analysis of Search and Selection V. SCHUMPETERIAN COMPETITION 12. Dynamic Competition and Technical Progress 13. Forces Generating and Limiting Concentration under Schumpeterian Competition 14. The Schumpeterian Tradeoff Revisited VI. ECONOMIC WELFARE AND POLICY 15. Normative Economics from an Evolutionary Perspective 16. The Evolution of Public Policies and the Role of Analysis VII. CONCLUSION 17. Retrospect and Prospect References Index

@book{openalexw3124140110,
    author = "Winter and Sidney, G",
    title = "An evolutionary theory of economic change",
    year = "1982",
    abstract = "I. OVERVIEW AND MOTIVATION 1. Introduction 2. The Need for an Evolutionary Theory II. ORGANIZATION-THEORETIC FOUNDATIONS OF ECONOMIC EVOLUTIONARY THEORY 3. The Foundations of Contemporary Orthodoxy 4. Skills 5. Organizational Capabilities and Behavior III. TEXTBOOK ECONOMICS REVISITED 6. Static Selection Equilibrium 7. Firm and Industry Response to Changed Market Conditions IV. GROWTH THEORY 8. Neoclassical Growth Theory: A Critique 9. An Evolutionary Model of Economic Growth 10. Economic Growth as a Pure Selection Process 11. Further Analysis of Search and Selection V. SCHUMPETERIAN COMPETITION 12. Dynamic Competition and Technical Progress 13. Forces Generating and Limiting Concentration under Schumpeterian Competition 14. The Schumpeterian Tradeoff Revisited VI. ECONOMIC WELFARE AND POLICY 15. Normative Economics from an Evolutionary Perspective 16. The Evolution of Public Policies and the Role of Analysis VII. CONCLUSION 17. Retrospect and Prospect References Index",
    url = "https://openalex.org/W3124140110",
    openalex = "W3124140110"
}

Abstract Contemporary mate preferences can provide important clues to human reproductive history. Little is known about which characteristics people value in potential mates. Five predictions were made about sex differences in human mate preferences based on evolutionary conceptions of parental investment, sexual selection, human reproductive capacity, and sexual asymmetries regarding certainty of paternity versus maternity. The predictions centered on how each sex valued earning capacity, ambition— industriousness, youth, physical attractiveness, and chastity. Predictions were tested in data from 37 samples drawn from 33 countries located on six continents and five islands (total N = 10,047). For 27 countries, demographic data on actual age at marriage provided a validity check on questionnaire data. Females were found to value cues to resource acquisition in potential mates more highly than males. Characteristics signaling reproductive capacity were valued more by males than by females. These sex differences may reflect different evolutionary selection pressures on human males and females; they provide powerful cross-cultural evidence of current sex differences in reproductive strategies. Discussion focuses on proximate mechanisms underlying mate preferences, consequences for human intrasexual competition, and the limitations of this study.

@article{doi101017s0140525x00023992,
    author = "Buss, David M.",
    title = "Sex differences in human mate preferences: Evolutionary hypotheses tested in 37 cultures",
    year = "1989",
    journal = "Behavioral and Brain Sciences",
    abstract = "Abstract Contemporary mate preferences can provide important clues to human reproductive history. Little is known about which characteristics people value in potential mates. Five predictions were made about sex differences in human mate preferences based on evolutionary conceptions of parental investment, sexual selection, human reproductive capacity, and sexual asymmetries regarding certainty of paternity versus maternity. The predictions centered on how each sex valued earning capacity, ambition— industriousness, youth, physical attractiveness, and chastity. Predictions were tested in data from 37 samples drawn from 33 countries located on six continents and five islands (total N = 10,047). For 27 countries, demographic data on actual age at marriage provided a validity check on questionnaire data. Females were found to value cues to resource acquisition in potential mates more highly than males. Characteristics signaling reproductive capacity were valued more by males than by females. These sex differences may reflect different evolutionary selection pressures on human males and females; they provide powerful cross-cultural evidence of current sex differences in reproductive strategies. Discussion focuses on proximate mechanisms underlying mate preferences, consequences for human intrasexual competition, and the limitations of this study.",
    url = "https://doi.org/10.1017/s0140525x00023992",
    doi = "10.1017/s0140525x00023992",
    openalex = "W2157338817",
    references = "doi101007978146847862422, doi1010160022519364900384, doi1010160022519366901846, doi1010160162309582900279, doi1010160162309583900274, doi101016s0065260122x00026, doi101017cbo9780511806292, doi101017s0140525x00010128, doi10103711774000, doi10103712293000, doi101038246015a0, doi101038369716c0, doi101086284064, doi101111j155856461957tb02911x, doi101126science327542, doi1011425786, doi1011770022022190211001, doi101537ase188722495, doi1023072393017, doi1023072412191, doi1023072485224, doi1023072576242, doi1023075530, doi102307582242, doi1043249781315129266, doi10432497813151292667, doi1043249781410606266, doi105962bhltitle27468, doi105962bhltitle59991, doi105962bhltitle82303, openalexw1649242647, openalexw2000871817"
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The mathematical characterization of singularities with Lipschitz exponents is reviewed. Theorems that estimate local Lipschitz exponents of functions from the evolution across scales of their wavelet transform are reviewed. It is then proven that the local maxima of the wavelet transform modulus detect the locations of irregular structures and provide numerical procedures to compute their Lipschitz exponents. The wavelet transform of singularities with fast oscillations has a particular behavior that is studied separately. The local frequency of such oscillations is measured from the wavelet transform modulus maxima. It has been shown numerically that one- and two-dimensional signals can be reconstructed, with a good approximation, from the local maxima of their wavelet transform modulus. As an application, an algorithm is developed that removes white noises from signals by analyzing the evolution of the wavelet transform maxima across scales. In two dimensions, the wavelet transform maxima indicate the location of edges in images. >

@article{doi10110918119727,
    author = "Mallat, Stéphane and Hwang, Wen-Liang",
    title = "Singularity detection and processing with wavelets",
    year = "1992",
    journal = "IEEE Transactions on Information Theory",
    abstract = "The mathematical characterization of singularities with Lipschitz exponents is reviewed. Theorems that estimate local Lipschitz exponents of functions from the evolution across scales of their wavelet transform are reviewed. It is then proven that the local maxima of the wavelet transform modulus detect the locations of irregular structures and provide numerical procedures to compute their Lipschitz exponents. The wavelet transform of singularities with fast oscillations has a particular behavior that is studied separately. The local frequency of such oscillations is measured from the wavelet transform modulus maxima. It has been shown numerically that one- and two-dimensional signals can be reconstructed, with a good approximation, from the local maxima of their wavelet transform modulus. As an application, an algorithm is developed that removes white noises from signals by analyzing the evolution of the wavelet transform maxima across scales. In two dimensions, the wavelet transform maxima indicate the location of edges in images. >",
    url = "https://doi.org/10.1109/18.119727",
    doi = "10.1109/18.119727",
    openalex = "W2152328854",
    references = "doi1023072981858"
}

@book{doi1010079781461540809,
    author = "Roff, Derek A.",
    title = "Evolutionary Quantitative Genetics",
    year = "1997",
    url = "https://doi.org/10.1007/978-1-4615-4080-9",
    doi = "10.1007/978-1-4615-4080-9",
    openalex = "W4229801755"
}

Evolutionary computation has started to receive significant attention during the last decade, although the origins can be traced back to the late 1950's. This article surveys the history as well as the current state of this rapidly growing field. We describe the purpose, the general structure, and the working principles of different approaches, including genetic algorithms (GA) (with links to genetic programming (GP) and classifier systems (CS)), evolution strategies (ES), and evolutionary programming (EP) by analysis and comparison of their most important constituents (i.e. representations, variation operators, reproduction, and selection mechanism). Finally, we give a brief overview on the manifold of application domains, although this necessarily must remain incomplete.

@article{doi1011094235585888,
    author = "Bäck, Thomas and Hammel, Ulrich and Schwefel, Hans–Paul",
    title = "Evolutionary computation: comments on the history and current state",
    year = "1997",
    journal = "IEEE Transactions on Evolutionary Computation",
    abstract = "Evolutionary computation has started to receive significant attention during the last decade, although the origins can be traced back to the late 1950's. This article surveys the history as well as the current state of this rapidly growing field. We describe the purpose, the general structure, and the working principles of different approaches, including genetic algorithms (GA) (with links to genetic programming (GP) and classifier systems (CS)), evolution strategies (ES), and evolutionary programming (EP) by analysis and comparison of their most important constituents (i.e. representations, variation operators, reproduction, and selection mechanism). Finally, we give a brief overview on the manifold of application domains, although this necessarily must remain incomplete.",
    url = "https://doi.org/10.1109/4235.585888",
    doi = "10.1109/4235.585888",
    openalex = "W2105217850",
    references = "doi1010079783662033159"
}

Preface. Introduction. Heritability. The genetic correlation. Directional selection. Directional selection and the correlated response. Directional selection and the correlated response. Phenotypic plasticity and reaction norms. Sex-related effects on quantitative variation. Bottlenecks, finite populations, and inbreeding.The maintenance of genetic variation. A summing up. Glossary of terms. Glossary of symbols. References. Subject index. Taxonomic index.

@article{doi105860choice355054,
    title = "Evolutionary quantitative genetics",
    year = "1998",
    journal = "Choice Reviews Online",
    abstract = "Preface. Introduction. Heritability. The genetic correlation. Directional selection. Directional selection and the correlated response. Directional selection and the correlated response. Phenotypic plasticity and reaction norms. Sex-related effects on quantitative variation. Bottlenecks, finite populations, and inbreeding.The maintenance of genetic variation. A summing up. Glossary of terms. Glossary of symbols. References. Subject index. Taxonomic index.",
    url = "https://doi.org/10.5860/choice.35-5054",
    doi = "10.5860/choice.35-5054",
    openalex = "W618189762"
}

Evolutionary algorithms (EAs) are often well-suited for optimization problems involving several, often conflicting objectives. Since 1985, various evolutionary approaches to multiobjective optimization have been developed that are capable of searching for multiple solutions concurrently in a single run. However, the few comparative studies of different methods presented up to now remain mostly qualitative and are often restricted to a few approaches. In this paper, four multiobjective EAs are compared quantitatively where an extended 0/1 knapsack problem is taken as a basis. Furthermore, we introduce a new evolutionary approach to multicriteria optimization, the strength Pareto EA (SPEA), that combines several features of previous multiobjective EAs in a unique manner. It is characterized by (a) storing nondominated solutions externally in a second, continuously updated population, (b) evaluating an individual's fitness dependent on the number of external nondominated points that dominate it, (c) preserving population diversity using the Pareto dominance relationship, and (d) incorporating a clustering procedure in order to reduce the nondominated set without destroying its characteristics. The proof-of-principle results obtained on two artificial problems as well as a larger problem, the synthesis of a digital hardware-software multiprocessor system, suggest that SPEA can be very effective in sampling from along the entire Pareto-optimal front and distributing the generated solutions over the tradeoff surface. Moreover, SPEA clearly outperforms the other four multiobjective EAs on the 0/1 knapsack problem.

@article{doi1011094235797969,
    author = "Zitzler, Eckart and Thiele, Lothar",
    title = "Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach",
    year = "1999",
    journal = "IEEE Transactions on Evolutionary Computation",
    abstract = "Evolutionary algorithms (EAs) are often well-suited for optimization problems involving several, often conflicting objectives. Since 1985, various evolutionary approaches to multiobjective optimization have been developed that are capable of searching for multiple solutions concurrently in a single run. However, the few comparative studies of different methods presented up to now remain mostly qualitative and are often restricted to a few approaches. In this paper, four multiobjective EAs are compared quantitatively where an extended 0/1 knapsack problem is taken as a basis. Furthermore, we introduce a new evolutionary approach to multicriteria optimization, the strength Pareto EA (SPEA), that combines several features of previous multiobjective EAs in a unique manner. It is characterized by (a) storing nondominated solutions externally in a second, continuously updated population, (b) evaluating an individual's fitness dependent on the number of external nondominated points that dominate it, (c) preserving population diversity using the Pareto dominance relationship, and (d) incorporating a clustering procedure in order to reduce the nondominated set without destroying its characteristics. The proof-of-principle results obtained on two artificial problems as well as a larger problem, the synthesis of a digital hardware-software multiprocessor system, suggest that SPEA can be very effective in sampling from along the entire Pareto-optimal front and distributing the generated solutions over the tradeoff surface. Moreover, SPEA clearly outperforms the other four multiobjective EAs on the 0/1 knapsack problem.",
    url = "https://doi.org/10.1109/4235.797969",
    doi = "10.1109/4235.797969",
    openalex = "W2106334424",
    references = "doi105860choice270936, openalexw1639032689"
}

@article{doi101023a1007677805582,
    author = "Michalski, Ryszard S.",
    title = "LEARNABLE EVOLUTION MODEL: Evolutionary Processes Guided by Machine Learning",
    year = "2000",
    journal = "Machine Learning",
    url = "https://doi.org/10.1023/a:1007677805582",
    doi = "10.1023/a:1007677805582",
    openalex = "W1587104749",
    references = "doi1010079783662028308, doi1010079783662033159, doi101016b9781558603776500232, doi105860choice270936, doi105962bhltitle59991, doi105962bhltitle68064, doi105962bhltitle82303, doi107551mitpress10900010001, doi107551mitpress39270010001, openalexw1639032689"
}

@article{doi101109cec2000870267,
    title = "Proceedings of the 2000 Congress on Evolutionary Computation",
    year = "2000",
    url = "https://doi.org/10.1109/cec.2000.870267",
    doi = "10.1109/cec.2000.870267",
    openalex = "W4298110920"
}

In this paper, we provide a systematic comparison of various evolutionary approaches to multiobjective optimization using six carefully chosen test functions. Each test function involves a particular feature that is known to cause difficulty in the evolutionary optimization process, mainly in converging to the Pareto-optimal front (e.g., multimodality and deception). By investigating these different problem features separately, it is possible to predict the kind of problems to which a certain technique is or is not well suited. However, in contrast to what was suspected beforehand, the experimental results indicate a hierarchy of the algorithms under consideration. Furthermore, the emerging effects are evidence that the suggested test functions provide sufficient complexity to compare multiobjective optimizers. Finally, elitism is shown to be an important factor for improving evolutionary multiobjective search.

@article{doi101162106365600568202,
    author = "Zitzler, Eckart and Deb, Kalyanmoy and Thiele, Lothar",
    title = "Comparison of Multiobjective Evolutionary Algorithms: Empirical Results",
    year = "2000",
    journal = "Evolutionary Computation",
    abstract = "In this paper, we provide a systematic comparison of various evolutionary approaches to multiobjective optimization using six carefully chosen test functions. Each test function involves a particular feature that is known to cause difficulty in the evolutionary optimization process, mainly in converging to the Pareto-optimal front (e.g., multimodality and deception). By investigating these different problem features separately, it is possible to predict the kind of problems to which a certain technique is or is not well suited. However, in contrast to what was suspected beforehand, the experimental results indicate a hierarchy of the algorithms under consideration. Furthermore, the emerging effects are evidence that the suggested test functions provide sufficient complexity to compare multiobjective optimizers. Finally, elitism is shown to be an important factor for improving evolutionary multiobjective search.",
    url = "https://doi.org/10.1162/106365600568202",
    doi = "10.1162/106365600568202",
    openalex = "W2125899728"
}

@article{doi10100797894010058529,
    author = "Gingerich, Philip D.",
    title = "Rates of evolution on the time scale of the evolutionary process",
    year = "2001",
    journal = "Contemporary issues in genetics and evolution",
    url = "https://doi.org/10.1007/978-94-010-0585-2\_9",
    doi = "10.1007/978-94-010-0585-2\_9",
    openalex = "W2116132452",
    references = "doi101093aibsbulletin2214b, doi101111j155856461976tb00911x, doi101111j155856461983tb00236x, doi101119113295, doi101126science1563775636, doi1023071435536, doi1023072408842, doi1023072981858, doi105860choice355054, openalexw3135630760"
}

@article{doi101023a1013311015886,
    author = "Gingerich, Philip D.",
    title = "Rates of evolution on the time scale of the evolutionary process",
    year = "2001",
    journal = "Genetica",
    url = "https://doi.org/10.1023/a:1013311015886",
    doi = "10.1023/a:1013311015886",
    openalex = "W4243454031",
    references = "doi1010079781461540809, doi101038387173a0, doi101086628623, doi101126science1563775636, doi101126science27553081934, doi1023071435536, doi1023072407703, doi1023072408842, doi1023072981858, openalexw3135630760"
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From the Publisher: Evolutionary algorithms are relatively new, but very powerful techniques used to find solutions to many real-world search and optimization problems. Many of these problems have multiple objectives, which leads to the need to obtain a set of optimal solutions, known as effective solutions. It has been found that using evolutionary algorithms is a highly effective way of finding multiple effective solutions in a single simulation run. · Comprehensive coverage of this growing area of research · Carefully introduces each algorithm with examples and in-depth discussion · Includes many applications to real-world problems, including engineering design and scheduling · Includes discussion of advanced topics and future research · Features exercises and solutions, enabling use as a course text or for self-study · Accessible to those with limited knowledge of classical multi-objective optimization and evolutionary algorithms The integrated presentation of theory, algorithms and examples will benefit those working and researching in the areas of optimization, optimal design and evolutionary computing. This text provides an excellent introduction to the use of evolutionary algorithms in multi-objective optimization, allowing use as a graduate course text or for self-study.

@book{openalexw1595498733,
    author = "Deb, Kalyanmoy and Kalyanmoy, Deb",
    title = "Multi-Objective Optimization Using Evolutionary Algorithms",
    year = "2001",
    booktitle = "Andalas University Repository (Andalas University)",
    abstract = "From the Publisher: Evolutionary algorithms are relatively new, but very powerful techniques used to find solutions to many real-world search and optimization problems. Many of these problems have multiple objectives, which leads to the need to obtain a set of optimal solutions, known as effective solutions. It has been found that using evolutionary algorithms is a highly effective way of finding multiple effective solutions in a single simulation run. · Comprehensive coverage of this growing area of research · Carefully introduces each algorithm with examples and in-depth discussion · Includes many applications to real-world problems, including engineering design and scheduling · Includes discussion of advanced topics and future research · Features exercises and solutions, enabling use as a course text or for self-study · Accessible to those with limited knowledge of classical multi-objective optimization and evolutionary algorithms The integrated presentation of theory, algorithms and examples will benefit those working and researching in the areas of optimization, optimal design and evolutionary computing. This text provides an excellent introduction to the use of evolutionary algorithms in multi-objective optimization, allowing use as a graduate course text or for self-study.",
    openalex = "W1595498733",
    references = "doi1010029780470172254, doi1010079780387367972, doi1010079781461555636, doi1010079783662028308, doi1011094235996017, doi101109tevc2003810758, doi103929ethza004284029, doi107551mitpress10900010001, doi107551mitpress39270010001, openalexw225560312"
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@article{doi101016s0045782501003231,
    author = "Coello, Carlos A. Coello",
    title = "Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art",
    year = "2002",
    journal = "Computer Methods in Applied Mechanics and Engineering",
    url = "https://doi.org/10.1016/s0045-7825(01)00323-1",
    doi = "10.1016/s0045-7825(01)00323-1",
    openalex = "W2167580870",
    references = "doi1010079783662028308, doi1010160378475482901173, doi105962bhltitle59991, doi107551mitpress39270010001, openalexw1511493290"
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@article{doi101016s0169534702025545,
    author = "Lee, Carol Eunmi",
    title = "Evolutionary genetics of invasive species",
    year = "2002",
    journal = "Trends in Ecology \& Evolution",
    url = "https://doi.org/10.1016/s0169-5347(02)02554-5",
    doi = "10.1016/s0169-5347(02)02554-5",
    openalex = "W2155928419",
    references = "doi101038sjhdy6886170, doi101073pnas97137043, doi101073pnas97137051, doi101111j155856461951tb02788x, doi101126science2925517673, doi1016410006356820000500053eaecon23co2, doi1023072261425, doi1023072265769, doi1023072412809, doi104159harvard9780674865327, openalexw1554403518, openalexw1584633894"
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This paper contains a modern vision of the parallelization techniques used for evolutionary algorithms (EAs). The work is motivated by two fundamental facts: 1) the different families of EAs have naturally converged in the last decade while parallel EAs (PEAs) are still lack of unified studies; and 2) there is a large number of improvements in these algorithms and in their parallelization that raise the need for a comprehensive survey. We stress the differences between the EA model and its parallel implementation throughout the paper. We discuss the advantages and drawbacks of PEAs. Also, successful applications are mentioned and open problems are identified. We propose potential solutions to these problems and classify the different ways in which recent results in theory and practice are helping to solve them. Finally, we provide a highly structured background relating to PEAs in order to make researchers aware of the benefits of decentralizing and parallelizing an EA.

@article{doi101109tevc2002800880,
    author = "Alba, Enrique and Tomassini, Marco",
    title = "Parallelism and evolutionary algorithms",
    year = "2002",
    journal = "IEEE Transactions on Evolutionary Computation",
    abstract = "This paper contains a modern vision of the parallelization techniques used for evolutionary algorithms (EAs). The work is motivated by two fundamental facts: 1) the different families of EAs have naturally converged in the last decade while parallel EAs (PEAs) are still lack of unified studies; and 2) there is a large number of improvements in these algorithms and in their parallelization that raise the need for a comprehensive survey. We stress the differences between the EA model and its parallel implementation throughout the paper. We discuss the advantages and drawbacks of PEAs. Also, successful applications are mentioned and open problems are identified. We propose potential solutions to these problems and classify the different ways in which recent results in theory and practice are helping to solve them. Finally, we provide a highly structured background relating to PEAs in order to make researchers aware of the benefits of decentralizing and parallelizing an EA.",
    url = "https://doi.org/10.1109/tevc.2002.800880",
    doi = "10.1109/tevc.2002.800880",
    openalex = "W2166348281",
    references = "doi1010079783662033159"
}

Traditionally, assignment and scheduling decisions are made separately at different levels of the production management framework. The combining of such decisions presents additional complexity and new problems. We present two new approaches to solve jointly the assignment and job-shop scheduling problems (with total or partial flexibility). The first one is the approach by localization (AL). It makes it possible to solve the problem of resource allocation and build an ideal assignment model (assignments schemata). The second one is an evolutionary approach controlled by the assignment model (generated by the first approach). In such an approach, we apply advanced genetic manipulations in order to enhance the solution quality. We also explain some of the practical and theoretical considerations in the construction of a more robust encoding that will enable us to solve the flexible job-shop problem by applying the genetic algorithms (GAs). Two examples are presented to show the efficiency of the two suggested methodologies.

@article{doi101109tsmcc20021009117,
    author = "Kacem, Imed and Hammadi, Slim and Borne, Pierre",
    title = "Approach by localization and multiobjective evolutionary optimization for flexible job-shop scheduling problems",
    year = "2002",
    journal = "IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews)",
    abstract = "Traditionally, assignment and scheduling decisions are made separately at different levels of the production management framework. The combining of such decisions presents additional complexity and new problems. We present two new approaches to solve jointly the assignment and job-shop scheduling problems (with total or partial flexibility). The first one is the approach by localization (AL). It makes it possible to solve the problem of resource allocation and build an ideal assignment model (assignments schemata). The second one is an evolutionary approach controlled by the assignment model (generated by the first approach). In such an approach, we apply advanced genetic manipulations in order to enhance the solution quality. We also explain some of the practical and theoretical considerations in the construction of a more robust encoding that will enable us to solve the flexible job-shop problem by applying the genetic algorithms (GAs). Two examples are presented to show the efficiency of the two suggested methodologies.",
    url = "https://doi.org/10.1109/tsmcc.2002.1009117",
    doi = "10.1109/tsmcc.2002.1009117",
    openalex = "W2166900052"
}

@book{doi102307jctvjsf433,
    author = "Gould, Stephen Jay",
    title = "The Structure of Evolutionary Theory",
    year = "2002",
    booktitle = "Harvard University Press eBooks",
    url = "https://doi.org/10.2307/j.ctvjsf433",
    doi = "10.2307/j.ctvjsf433",
    openalex = "W4300925890"
}

* *1. Defining and Revising the Structure of Evolutionary Theory * Part I: The History of Darwinian Logic and Debate *2. The Essence of Darwinism and the Basis of Modern Orthodoxy: An Exegesis of the Origin of Species *3. Seeds of Hierarchy *4. Internalism and Laws of Form: Pre-Darwinian Alternatives to Functionalism *5. The Fruitful Facets of Galton's Polyhedron: Channels and Saltations in Post-Darwinian Formalism *6. Pattern and Progress on the Geological Stage *7. The Modern Synthesis as a Limited Consensus * Part II: Towards a Revised and Expanded Evolutionary Theory *8. Species as Individuals in the Hierarchical Theory of Selection *9. Punctuated Equilibrium and the Validation of Macroevolutionary Theory *10. The Integration of Constraint and Adaptation (Structure and Function) in Ontogeny and Phylogeny: Historical Constraints and the Evolution of Development *11. The Integration of Constraint and Adaptation (Structure and Function) in Ontogeny and Phylogeny: Structural Constraints, Spandrels, and the Centrality of Exaptation in Macroevolution *12. Tiers of Time and Trials of Extrapolationism, With an Epilog on the Interaction of General Theory and Contingent History * Bibliography * Index

@article{doi105860choice396411,
    author = "Gould, Stephen Jay",
    title = "The structure of evolutionary theory",
    year = "2002",
    journal = "Choice Reviews Online",
    abstract = "* *1. Defining and Revising the Structure of Evolutionary Theory * Part I: The History of Darwinian Logic and Debate *2. The Essence of Darwinism and the Basis of Modern Orthodoxy: An Exegesis of the Origin of Species *3. Seeds of Hierarchy *4. Internalism and Laws of Form: Pre-Darwinian Alternatives to Functionalism *5. The Fruitful Facets of Galton's Polyhedron: Channels and Saltations in Post-Darwinian Formalism *6. Pattern and Progress on the Geological Stage *7. The Modern Synthesis as a Limited Consensus * Part II: Towards a Revised and Expanded Evolutionary Theory *8. Species as Individuals in the Hierarchical Theory of Selection *9. Punctuated Equilibrium and the Validation of Macroevolutionary Theory *10. The Integration of Constraint and Adaptation (Structure and Function) in Ontogeny and Phylogeny: Historical Constraints and the Evolution of Development *11. The Integration of Constraint and Adaptation (Structure and Function) in Ontogeny and Phylogeny: Structural Constraints, Spandrels, and the Centrality of Exaptation in Macroevolution *12. Tiers of Time and Trials of Extrapolationism, With an Epilog on the Interaction of General Theory and Contingent History * Bibliography * Index",
    url = "https://doi.org/10.5860/choice.39-6411",
    doi = "10.5860/choice.39-6411",
    openalex = "W1539968307"
}

@article{doi101016jtree200309010,
    author = "Olden, Julian D. and Poff, N. LeRoy and Douglas, Marlis R. and Douglas, Michael E. and Fausch, Kurt D.",
    title = "Ecological and evolutionary consequences of biotic homogenization",
    year = "2003",
    journal = "Trends in Ecology \& Evolution",
    url = "https://doi.org/10.1016/j.tree.2003.09.010",
    doi = "10.1016/j.tree.2003.09.010",
    openalex = "W2159330259",
    references = "doi1010079789400958517, doi101016s0169534701022832, doi101016s0169534702000447, doi101016s0169534702025545, doi101016s0169534703000089, doi101016s0169534703001009, doi101016s0169534799016791, doi101046j13652745200000473x, doi101111j155856461954tb01504x, doi101111j155856461987tb02459x, doi101126science25350241099, doi101126science27753301300, doi101126science2885467854, doi101146annurevecolsys27183, doi1015159780691209418, doi1023072257385, doi1023072409086, doi102307jctvx5wbbh"
}

Recently, there has been increased interest in evolutionary computation applied to changing optimization problems. The paper surveys a number of approaches that extend the evolutionary algorithm with implicit or explicit memory, suggests a new benchmark problem and examines under which circumstances a memory may be helpful. From these observations, we derive a new way to explore the benefits of a memory while minimizing its negative side effects.

@article{doi101109cec1999785502,
    author = "Branke, Juergen",
    title = "Memory enhanced evolutionary algorithms for changing optimization problems",
    year = "2003",
    abstract = "Recently, there has been increased interest in evolutionary computation applied to changing optimization problems. The paper surveys a number of approaches that extend the evolutionary algorithm with implicit or explicit memory, suggests a new benchmark problem and examines under which circumstances a memory may be helpful. From these observations, we derive a new way to explore the benefits of a memory while minimizing its negative side effects.",
    url = "https://doi.org/10.1109/cec.1999.785502",
    doi = "10.1109/cec.1999.785502",
    openalex = "W2148458253"
}

Evolutionary Phonology is a theory of sound patterns which synthesizes results in historical linguistics, phonetics and phonological theory. In this book, Juliette Blevins explores the nature of sounds patterns and sound change in human language over the past 7000–8000 years, the time depth for which the comparative method is reasonably reliable. This book presents an approach to the problem of how genetically unrelated languages, from families as far apart as Native American, Australian Aboriginal, Austronesian and Indo-European, can often show similar sound patterns, and also tackles the converse problem of why there are notable exceptions to most of the patterns that are often regarded as universal tendencies or constraints. It argues that in both cases, a formal model of sound change that integrates phonetic variation and patterns of misperception can account for attested sound systems without reference to markedness or naturalness within the synchronic grammar

@book{doi101017cbo9780511486357,
    author = "Blevins, Juliette",
    title = "Evolutionary phonology the emergence of sound patterns",
    year = "2004",
    abstract = "Evolutionary Phonology is a theory of sound patterns which synthesizes results in historical linguistics, phonetics and phonological theory. In this book, Juliette Blevins explores the nature of sounds patterns and sound change in human language over the past 7000–8000 years, the time depth for which the comparative method is reasonably reliable. This book presents an approach to the problem of how genetically unrelated languages, from families as far apart as Native American, Australian Aboriginal, Austronesian and Indo-European, can often show similar sound patterns, and also tackles the converse problem of why there are notable exceptions to most of the patterns that are often regarded as universal tendencies or constraints. It argues that in both cases, a formal model of sound change that integrates phonetic variation and patterns of misperception can account for attested sound systems without reference to markedness or naturalness within the synchronic grammar",
    url = "https://doi.org/10.1017/cbo9780511486357",
    doi = "10.1017/cbo9780511486357",
    openalex = "W1493009933",
    references = "doi101038343066a0, doi105962bhltitle68064"
}

Abstract Recent studies have documented rates of evolution of ecologically important phenotypes sufficiently fast that they have the potential to impact the outcome of ecological interactions while they are underway. Observations of this type go against accepted wisdom that ecological and evolutionary dynamics occur at very different time scales. While some authors have evaluated the rapidity of a measured evolutionary rate by comparing it to the overall distribution of measured evolutionary rates, we believe that ecologists are mainly interested in rapid evolution because of its potential to impinge on ecological processes. We therefore propose that rapid evolution be defined as a genetic change occurring rapidly enough to have a measurable impact on simultaneous ecological change. Using this definition we propose a framework for decomposing rates of ecological change into components driven by simultaneous evolutionary change and by change in a non‐evolutionary factor (e.g. density dependent population dynamics, abiotic environmental change). Evolution is judged to be rapid in this ecological context if its contribution to ecological change is large relative to the contribution of other factors. We provide a worked example of this approach based on a theoretical predator–prey interaction [Abrams, P. & Matsuda, H. (1997). Evolution, 51, 1740], and find that in this system the impact of prey evolution on predator per capita growth rate is 63% that of internal ecological dynamics. We then propose analytical methods for measuring these contributions in field situations, and apply them to two long‐term data sets for which suitable ecological and evolutionary data exist. For both data sets relatively high rates of evolutionary change have been found when measured as character change in standard deviations per generation (haldanes). For Darwin's finches evolving in response to fluctuating rainfall [Grant, P.R. & Grant, B.R. (2002). Science, 296, 707], we estimate that evolutionary change has been more rapid than ecological change by a factor of 2.2. For a population of freshwater copepods whose life history evolves in response to fluctuating fish predation [Hairston, N.G. Jr & Dillon, T.A. (1990). Evolution, 44, 1796], we find that evolutionary change has been about one quarter the rate of ecological change – less than in the finch example, but nevertheless substantial. These analyses support the view that in order to understand temporal dynamics in ecological processes it is critical to consider the extent to which the attributes of the system under investigation are simultaneously changing as a result of rapid evolution.

@article{doi101111j14610248200500812x,
    author = "Hairston, Nelson G. and Ellner, Stephen P. and Geber, Monica A. and Yoshida, Takehito and Fox, Jennifer",
    title = "Rapid evolution and the convergence of ecological and evolutionary time",
    year = "2005",
    journal = "Ecology Letters",
    abstract = "Abstract Recent studies have documented rates of evolution of ecologically important phenotypes sufficiently fast that they have the potential to impact the outcome of ecological interactions while they are underway. Observations of this type go against accepted wisdom that ecological and evolutionary dynamics occur at very different time scales. While some authors have evaluated the rapidity of a measured evolutionary rate by comparing it to the overall distribution of measured evolutionary rates, we believe that ecologists are mainly interested in rapid evolution because of its potential to impinge on ecological processes. We therefore propose that rapid evolution be defined as a genetic change occurring rapidly enough to have a measurable impact on simultaneous ecological change. Using this definition we propose a framework for decomposing rates of ecological change into components driven by simultaneous evolutionary change and by change in a non‐evolutionary factor (e.g. density dependent population dynamics, abiotic environmental change). Evolution is judged to be rapid in this ecological context if its contribution to ecological change is large relative to the contribution of other factors. We provide a worked example of this approach based on a theoretical predator–prey interaction [Abrams, P. \& Matsuda, H. (1997). Evolution, 51, 1740], and find that in this system the impact of prey evolution on predator per capita growth rate is 63\% that of internal ecological dynamics. We then propose analytical methods for measuring these contributions in field situations, and apply them to two long‐term data sets for which suitable ecological and evolutionary data exist. For both data sets relatively high rates of evolutionary change have been found when measured as character change in standard deviations per generation (haldanes). For Darwin's finches evolving in response to fluctuating rainfall [Grant, P.R. \& Grant, B.R. (2002). Science, 296, 707], we estimate that evolutionary change has been more rapid than ecological change by a factor of 2.2. For a population of freshwater copepods whose life history evolves in response to fluctuating fish predation [Hairston, N.G. Jr \& Dillon, T.A. (1990). Evolution, 44, 1796], we find that evolutionary change has been about one quarter the rate of ecological change – less than in the finch example, but nevertheless substantial. These analyses support the view that in order to understand temporal dynamics in ecological processes it is critical to consider the extent to which the attributes of the system under investigation are simultaneously changing as a result of rapid evolution.",
    url = "https://doi.org/10.1111/j.1461-0248.2005.00812.x",
    doi = "10.1111/j.1461-0248.2005.00812.x",
    openalex = "W1541870771",
    references = "doi10100797894010058529, doi1010160169534787900280, doi101023a1013311015886, doi101038nature01767, doi101093aibsbulletin2214b, doi101126science1070315, doi101126science2224620159, doi101126science2925517673, doi1015159781400885695, doi101890030788, doi1023071435536, doi1023071445906, doi1023074785, doi105860choice306153"
}

▪ Abstract Mixed mating, in which hermaphrodite plant species reproduce by both self- and cross-fertilization, presents a challenging problem for evolutionary biologists. Theory suggests that inbreeding depression, the main selective factor opposing the evolution of selfing, can be purged with self-fertilization, a process that is expected to yield pure strategies of either outcrossing or selfing. Here we present updated evidence suggesting that mixed mating systems are frequent in seed plants. We outline the floral and pollination mechanisms that can lead to intermediate outcrossing, review the theoretical models that address the stability of intermediate outcrossing, and examine relevant empirical evidence. A comparative analysis of estimated inbreeding coefficients and outcrossing rates suggests that mixed mating often evolves despite strong inbreeding depression. The adaptive significance of mixed mating has yet to be fully explained for any species. Recent theoretical and empirical work suggests that future progress will come from a better integration of studies of floral mechanisms, genetics, and ecology, and recognition of how selective pressures vary in space and time.

@article{doi101146annurevecolsys36091704175539,
    author = "Goodwillie, Carol and Kalisz, Susan and Eckert, Christopher G.",
    title = "The Evolutionary Enigma of Mixed Mating Systems in Plants: Occurrence, Theoretical Explanations, and Empirical Evidence",
    year = "2005",
    journal = "Annual Review of Ecology Evolution and Systematics",
    abstract = "▪ Abstract Mixed mating, in which hermaphrodite plant species reproduce by both self- and cross-fertilization, presents a challenging problem for evolutionary biologists. Theory suggests that inbreeding depression, the main selective factor opposing the evolution of selfing, can be purged with self-fertilization, a process that is expected to yield pure strategies of either outcrossing or selfing. Here we present updated evidence suggesting that mixed mating systems are frequent in seed plants. We outline the floral and pollination mechanisms that can lead to intermediate outcrossing, review the theoretical models that address the stability of intermediate outcrossing, and examine relevant empirical evidence. A comparative analysis of estimated inbreeding coefficients and outcrossing rates suggests that mixed mating often evolves despite strong inbreeding depression. The adaptive significance of mixed mating has yet to be fully explained for any species. Recent theoretical and empirical work suggests that future progress will come from a better integration of studies of floral mechanisms, genetics, and ecology, and recognition of how selective pressures vary in space and time.",
    url = "https://doi.org/10.1146/annurev.ecolsys.36.091704.175539",
    doi = "10.1146/annurev.ecolsys.36.091704.175539",
    openalex = "W2127588110",
    references = "doi10100797814615694425, doi101111j109583122000tb01221x, doi101146annureves15110184000433, doi105860choice355054, doi105962bhltitle110800, openalexw2491318968"
}

Until now, Quaternary paleoecologists have regarded evolution as a slow process relative to climate change, predicting that the primary biotic response to changing climate is not adaptation, but instead (1) persistence in situ if changing climate remains within the species' tolerance limits, (2) range shifts (migration) to regions where climate is currently within the species' tolerance limits, or (3) extinction. We argue here that all three of these outcomes involve evolutionary processes. Genetic differentiation within species is ubiquitous, commonly via adaptation of populations to differing environmental conditions. Detectable adaptive divergence evolves on a time scale comparable to change in climate, within decades for herbaceous plant species, and within centuries or millennia for longer-lived trees, implying that biologically significant evolutionary response can accompany temporal change in climate. Models and empirical studies suggest that the speed with which a population adapts to a changing environment affects invasion rate of new habitat and thus migration rate, population growth rate and thus probability of extinction, and growth and mortality of individual plants and thus productivity of regional vegetation. Recent models and experiments investigate the stability of species tolerance limits, the influence of environmental gradients on marginal populations, and the interplay of demography, gene flow, mutation rate, and other genetic processes on the rate of adaptation to changed environments. New techniques enable ecologists to document adaptation to changing conditions directly by resurrecting ancient populations from propagules buried in decades-old sediment. Improved taxonomic resolution from morphological studies of macrofossils and DNA recovered from pollen grains and macroremains provides additional information on range shifts, changes in population sizes, and extinctions. Collaboration between paleoecologists and evolutionary biologists can refine interpretations of paleorecords, and improve predictions of biotic response to anticipated climate change.

@article{doi101890030788,
    author = "Davis, Margaret B. and Shaw, Ruth G. and Etterson, Julie R.",
    title = "EVOLUTIONARY RESPONSES TO CHANGING CLIMATE",
    year = "2005",
    journal = "Ecology",
    abstract = "Until now, Quaternary paleoecologists have regarded evolution as a slow process relative to climate change, predicting that the primary biotic response to changing climate is not adaptation, but instead (1) persistence in situ if changing climate remains within the species' tolerance limits, (2) range shifts (migration) to regions where climate is currently within the species' tolerance limits, or (3) extinction. We argue here that all three of these outcomes involve evolutionary processes. Genetic differentiation within species is ubiquitous, commonly via adaptation of populations to differing environmental conditions. Detectable adaptive divergence evolves on a time scale comparable to change in climate, within decades for herbaceous plant species, and within centuries or millennia for longer-lived trees, implying that biologically significant evolutionary response can accompany temporal change in climate. Models and empirical studies suggest that the speed with which a population adapts to a changing environment affects invasion rate of new habitat and thus migration rate, population growth rate and thus probability of extinction, and growth and mortality of individual plants and thus productivity of regional vegetation. Recent models and experiments investigate the stability of species tolerance limits, the influence of environmental gradients on marginal populations, and the interplay of demography, gene flow, mutation rate, and other genetic processes on the rate of adaptation to changed environments. New techniques enable ecologists to document adaptation to changing conditions directly by resurrecting ancient populations from propagules buried in decades-old sediment. Improved taxonomic resolution from morphological studies of macrofossils and DNA recovered from pollen grains and macroremains provides additional information on range shifts, changes in population sizes, and extinctions. Collaboration between paleoecologists and evolutionary biologists can refine interpretations of paleorecords, and improve predictions of biotic response to anticipated climate change.",
    url = "https://doi.org/10.1890/03-0788",
    doi = "10.1890/03-0788",
    openalex = "W2155225809"
}

@article{doi101038nrc2013,
    author = "Merlo, Lauren M.F. and Pepper, John W. and Reid, Brian J. and Maley, Carlo C.",
    title = "Cancer as an evolutionary and ecological process",
    year = "2006",
    journal = "Nature reviews. Cancer",
    url = "https://doi.org/10.1038/nrc2013",
    doi = "10.1038/nrc2013",
    openalex = "W2123558100",
    references = "doi1010021521187820001222121057aidbies330co2w, doi101016s0169534701021012, doi101016s0169534702024953, doi10103842701, doi101038nrg1088, doi101146annurevgenet341401, doi1023072407274"
}

@book{doi1010079780387367972,
    author = "Coello Coello, Carlos A.",
    title = "Evolutionary Algorithms for Solving Multi-Objective Problems",
    year = "2007",
    url = "https://doi.org/10.1007/978-0-387-36797-2",
    doi = "10.1007/978-0-387-36797-2",
    openalex = "W1553373771",
    references = "doi1010029781119487142ch6, doi10100735404471999, doi10100797830302483524, doi10100797835402472272, doi10100797836427348302, doi1041359781071812082n394, doi109746sicetr1965351198"
}

We tested the ability of six quantitative genetic models to explain the evolution of phenotypic means using an extensive database compiled by Gingerich. Our approach differs from past efforts in that we use explicit models of evolutionary process, with parameters estimated from contemporary populations, to analyze a large sample of divergence data on many different timescales. We show that one quantitative genetic model yields a good fit to data on phenotypic divergence across timescales ranging from a few generations to 10 million generations. The key feature of this model is a fitness optimum that moves within fixed limits. Conversely, a model of neutral evolution, models with a stationary optimum that undergoes Brownian or white noise motion, a model with a moving optimum, and a peak shift model all fail to account for the data on most or all timescales. We discuss our results within the framework of Simpson's concept of adaptive landscapes and zones. Our analysis suggests that the underlying process causing phenotypic stasis is adaptation to an optimum that moves within an adaptive zone with stable boundaries. We discuss the implication of our results for comparative studies and phylogeny inference based on phenotypic characters.

@article{doi101086510633,
    author = "Estes, Suzanne and Arnold, Stevan J.",
    title = "Resolving the Paradox of Stasis: Models with Stabilizing Selection Explain Evolutionary Divergence on All Timescales",
    year = "2007",
    journal = "The American Naturalist",
    abstract = "We tested the ability of six quantitative genetic models to explain the evolution of phenotypic means using an extensive database compiled by Gingerich. Our approach differs from past efforts in that we use explicit models of evolutionary process, with parameters estimated from contemporary populations, to analyze a large sample of divergence data on many different timescales. We show that one quantitative genetic model yields a good fit to data on phenotypic divergence across timescales ranging from a few generations to 10 million generations. The key feature of this model is a fitness optimum that moves within fixed limits. Conversely, a model of neutral evolution, models with a stationary optimum that undergoes Brownian or white noise motion, a model with a moving optimum, and a peak shift model all fail to account for the data on most or all timescales. We discuss our results within the framework of Simpson's concept of adaptive landscapes and zones. Our analysis suggests that the underlying process causing phenotypic stasis is adaptation to an optimum that moves within an adaptive zone with stable boundaries. We discuss the implication of our results for comparative studies and phylogeny inference based on phenotypic characters.",
    url = "https://doi.org/10.1086/510633",
    doi = "10.1086/510633",
    openalex = "W2120631206",
    references = "doi10100797894010058529, doi101023a1013311015886, doi101038scientificamerican117998, doi101046j14390388200200356x, doi101086284325, doi101086383584, doi101111j155856461983tb00236x, doi1023072485224, doi102307jctvjsf433, doi105860choice396411, openalexw1591710988, openalexw3135630760"
}

This open source software is written entirely in the R language and is freely available through the Comprehensive R Archive Network (CRAN) at http://cran.r-project.org/.

@article{doi101093bioinformaticsbtm538,
    author = "Harmon, Luke J. and Weir, Jason T. and Brock, Chad D. and Glor, Richard E. and Challenger, Wendell",
    title = "GEIGER: investigating evolutionary radiations",
    year = "2007",
    journal = "Bioinformatics",
    abstract = "This open source software is written entirely in the R language and is freely available through the Comprehensive R Archive Network (CRAN) at http://cran.r-project.org/.",
    url = "https://doi.org/10.1093/bioinformatics/btm538",
    doi = "10.1093/bioinformatics/btm538",
    openalex = "W2117368100",
    references = "doi10103844766, doi101093oso97801985052350010001, doi101111j001438202001tb00826x"
}

Decomposition is a basic strategy in traditional multiobjective optimization. However, it has not yet been widely used in multiobjective evolutionary optimization. This paper proposes a multiobjective evolutionary algorithm based on decomposition (MOEA/D). It decomposes a multiobjective optimization problem into a number of scalar optimization subproblems and optimizes them simultaneously. Each subproblem is optimized by only using information from its several neighboring subproblems, which makes MOEA/D have lower computational complexity at each generation than MOGLS and nondominated sorting genetic algorithm II (NSGA-II). Experimental results have demonstrated that MOEA/D with simple decomposition methods outperforms or performs similarly to MOGLS and NSGA-II on multiobjective 0-1 knapsack problems and continuous multiobjective optimization problems. It has been shown that MOEA/D using objective normalization can deal with disparately-scaled objectives, and MOEA/D with an advanced decomposition method can generate a set of very evenly distributed solutions for 3-objective test instances. The ability of MOEA/D with small population, the scalability and sensitivity of MOEA/D have also been experimentally investigated in this paper.

@article{doi101109tevc2007892759,
    author = "Zhang, Qingfu and Li, Hui",
    title = "MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition",
    year = "2007",
    journal = "IEEE Transactions on Evolutionary Computation",
    abstract = "Decomposition is a basic strategy in traditional multiobjective optimization. However, it has not yet been widely used in multiobjective evolutionary optimization. This paper proposes a multiobjective evolutionary algorithm based on decomposition (MOEA/D). It decomposes a multiobjective optimization problem into a number of scalar optimization subproblems and optimizes them simultaneously. Each subproblem is optimized by only using information from its several neighboring subproblems, which makes MOEA/D have lower computational complexity at each generation than MOGLS and nondominated sorting genetic algorithm II (NSGA-II). Experimental results have demonstrated that MOEA/D with simple decomposition methods outperforms or performs similarly to MOGLS and NSGA-II on multiobjective 0-1 knapsack problems and continuous multiobjective optimization problems. It has been shown that MOEA/D using objective normalization can deal with disparately-scaled objectives, and MOEA/D with an advanced decomposition method can generate a set of very evenly distributed solutions for 3-objective test instances. The ability of MOEA/D with small population, the scalability and sensitivity of MOEA/D have also been experimentally investigated in this paper.",
    url = "https://doi.org/10.1109/tevc.2007.892759",
    doi = "10.1109/tevc.2007.892759",
    openalex = "W2143381319",
    references = "doi1010079780387367972, openalexw1595498733"
}

A recent advance in the phylogenetic comparative analysis of continuous traits has been explicit, model-based measurement of "phylogenetic signal" in data sets composed of observations collected from species related by a phylogenetic tree. Phylogenetic signal is a measure of the statistical dependence among species' trait values due to their phylogenetic relationships. Although phylogenetic signal is a measure of pattern (statistical dependence), there has nonetheless been a widespread propensity in the literature to attribute this pattern to aspects of the evolutionary process or rate. This may be due, in part, to the perception that high evolutionary rate necessarily results in low phylogenetic signal; and, conversely, that low evolutionary rate or stabilizing selection results in high phylogenetic signal (due to the resulting high resemblance between related species). In this study, we use individual-based numerical simulations on stochastic phylogenetic trees to clarify the relationship between phylogenetic signal, rate, and evolutionary process. Under the simplest model for quantitative trait evolution, homogeneous rate genetic drift, there is no relation between evolutionary rate and phylogenetic signal. For other circumstances, such as functional constraint, fluctuating selection, niche conservatism, and evolutionary heterogeneity, the relationship between process, rate, and phylogenetic signal is complex. For these reasons, we recommend against interpretations of evolutionary process or rate based on estimates of phylogenetic signal.

@article{doi10108010635150802302427,
    author = "Revell, Liam J. and Harmon, Luke J. and Collar, David C.",
    title = "Phylogenetic Signal, Evolutionary Process, and Rate",
    year = "2008",
    journal = "Systematic Biology",
    abstract = {A recent advance in the phylogenetic comparative analysis of continuous traits has been explicit, model-based measurement of "phylogenetic signal" in data sets composed of observations collected from species related by a phylogenetic tree. Phylogenetic signal is a measure of the statistical dependence among species' trait values due to their phylogenetic relationships. Although phylogenetic signal is a measure of pattern (statistical dependence), there has nonetheless been a widespread propensity in the literature to attribute this pattern to aspects of the evolutionary process or rate. This may be due, in part, to the perception that high evolutionary rate necessarily results in low phylogenetic signal; and, conversely, that low evolutionary rate or stabilizing selection results in high phylogenetic signal (due to the resulting high resemblance between related species). In this study, we use individual-based numerical simulations on stochastic phylogenetic trees to clarify the relationship between phylogenetic signal, rate, and evolutionary process. Under the simplest model for quantitative trait evolution, homogeneous rate genetic drift, there is no relation between evolutionary rate and phylogenetic signal. For other circumstances, such as functional constraint, fluctuating selection, niche conservatism, and evolutionary heterogeneity, the relationship between process, rate, and phylogenetic signal is complex. For these reasons, we recommend against interpretations of evolutionary process or rate based on estimates of phylogenetic signal.},
    url = "https://doi.org/10.1080/10635150802302427",
    doi = "10.1080/10635150802302427",
    openalex = "W2162399971",
    references = "doi101038217624a0, doi10103844766, doi101073pnas0507648103, doi101086284325, doi101086383584, doi101086510633, doi101093oso97801985052350010001, doi101093oso97801985464120010001, doi101111j001438202001tb00731x, doi101111j001438202003tb00285x, doi1023072529912, doi102307jctvjsf433, doi105860choice295104, doi105860choice396411"
}

It is impossible for human beings fully to understand either themselves or their long prehuman history without knowing something of the process (or, rather, processes) by which our remarkable species be- came what it is. This is, as (almost) everybody knows, evolution. And although most of us have a vague idea of what evolution is all about, few realize quite how many factors have typically been involved in the evolutionary histories that gave rise to the diversity of today’ s living world. For evolution is not, as we often believe, a simple, linear process; rather, it is an untidy affair involving many different causes and influences.

@incollection{tattersall2008evolutionary,
    author = "Tattersall, Ian",
    title = "Evolutionary Processes",
    year = "2008",
    booktitle = "The World from Beginnings to 4000 BCE",
    abstract = "It is impossible for human beings fully to understand either themselves or their long prehuman history without knowing something of the process (or, rather, processes) by which our remarkable species be- came what it is. This is, as (almost) everybody knows, evolution. And although most of us have a vague idea of what evolution is all about, few realize quite how many factors have typically been involved in the evolutionary histories that gave rise to the diversity of today’ s living world. For evolution is not, as we often believe, a simple, linear process; rather, it is an untidy affair involving many different causes and influences.",
    url = "https://doi.org/10.1093/oso/9780195167122.003.0001",
    doi = "10.1093/oso/9780195167122.003.0001",
    openalex = "W4388057727",
    pages = "1-17"
}

G. Evelyn Hutchinson more than a half century ago proposed that one could characterize the ecological niche of a species as an abstract mapping of population dynamics onto an environmental space, the axes of which are abiotic and biotic factors that influence birth and death rates. If a habitat has conditions within a species' niche, a population should persist without immigration from external sources, whereas if conditions are outside the niche, it faces extinction. Analyses of species' niches are essential to understanding controls on species' geographical range limits and how these limits might shift in our rapidly changing world. Recent developments in ecology and evolutionary biology suggest it is time to revisit and refine Hutchinson's niche concept. After reviewing techniques for quantifying niches, I examine subtleties that arise because of impacts species have on their own environments, the density-dependent modulation of how individuals experience environments, and the interplay of dispersal and temporal heterogeneity in determining population persistence. Moreover, the evolutionary record over all time scales reveals a spectrum of rates of change in species' niches, from rapid niche evolution to profound niche conservatism. Substantial challenges revolving around the evolutionary dimension of the Hutchinsonian niche include quantifying the magnitude of evolved intraspecific and clade-level variation in niches and understanding the factors that govern where along the spectrum of potential evolutionary rates any given lineage lies. A growing body of theory provides elements of a conceptual framework for understanding niche conservatism and evolution, paving the way for an evolutionary theory of the niche.

@article{doi101073pnas0905137106,
    author = "Holt, Robert D.",
    title = "Bringing the Hutchinsonian niche into the 21st century: Ecological and evolutionary perspectives",
    year = "2009",
    journal = "Proceedings of the National Academy of Sciences",
    abstract = "G. Evelyn Hutchinson more than a half century ago proposed that one could characterize the ecological niche of a species as an abstract mapping of population dynamics onto an environmental space, the axes of which are abiotic and biotic factors that influence birth and death rates. If a habitat has conditions within a species' niche, a population should persist without immigration from external sources, whereas if conditions are outside the niche, it faces extinction. Analyses of species' niches are essential to understanding controls on species' geographical range limits and how these limits might shift in our rapidly changing world. Recent developments in ecology and evolutionary biology suggest it is time to revisit and refine Hutchinson's niche concept. After reviewing techniques for quantifying niches, I examine subtleties that arise because of impacts species have on their own environments, the density-dependent modulation of how individuals experience environments, and the interplay of dispersal and temporal heterogeneity in determining population persistence. Moreover, the evolutionary record over all time scales reveals a spectrum of rates of change in species' niches, from rapid niche evolution to profound niche conservatism. Substantial challenges revolving around the evolutionary dimension of the Hutchinsonian niche include quantifying the magnitude of evolved intraspecific and clade-level variation in niches and understanding the factors that govern where along the spectrum of potential evolutionary rates any given lineage lies. A growing body of theory provides elements of a conceptual framework for understanding niche conservatism and evolution, paving the way for an evolutionary theory of the niche.",
    url = "https://doi.org/10.1073/pnas.0905137106",
    doi = "10.1073/pnas.0905137106",
    openalex = "W2015873058",
    references = "doi101086510633, openalexw332631162"
}

Interactions among conspecifics influence social evolution through two distinct but intimately related paths. First, they provide the opportunity for indirect genetic effects (IGEs), where genes expressed in one individual influence the expression of traits in others. Second, interactions can generate social selection when traits expressed in one individual influence the fitness of others. Here, we present a quantitative genetic model of multivariate trait evolution that integrates the effects of both IGEs and social selection, which have previously been modeled independently. We show that social selection affects evolutionary change whenever the breeding value of one individual covaries with the phenotype of its social partners. This covariance can be created by both relatedness and IGEs, which are shown to have parallel roles in determining evolutionary response. We show that social selection is central to the estimation of inclusive fitness and derive a version of Hamilton's rule showing the symmetrical effects of relatedness and IGEs on the evolution of altruism. We illustrate the utility of our approach using altruism, greenbeards, aggression, and weapons as examples. Our model provides a general predictive equation for the evolution of social phenotypes that encompasses specific cases such as kin selection and reciprocity. The parameters can be measured empirically, and we emphasize the importance of considering both IGEs and social selection, in addition to relatedness, when testing hypotheses about social evolution.

@article{doi101111j15585646201001012x,
    author = "McGlothlin, Joel W. and Moore, Allen J. and Wolf, Jason B. and Brodie, Edmund D.",
    title = "INTERACTING PHENOTYPES AND THE EVOLUTIONARY PROCESS. III. SOCIAL EVOLUTION",
    year = "2010",
    journal = "Evolution",
    abstract = "Interactions among conspecifics influence social evolution through two distinct but intimately related paths. First, they provide the opportunity for indirect genetic effects (IGEs), where genes expressed in one individual influence the expression of traits in others. Second, interactions can generate social selection when traits expressed in one individual influence the fitness of others. Here, we present a quantitative genetic model of multivariate trait evolution that integrates the effects of both IGEs and social selection, which have previously been modeled independently. We show that social selection affects evolutionary change whenever the breeding value of one individual covaries with the phenotype of its social partners. This covariance can be created by both relatedness and IGEs, which are shown to have parallel roles in determining evolutionary response. We show that social selection is central to the estimation of inclusive fitness and derive a version of Hamilton's rule showing the symmetrical effects of relatedness and IGEs on the evolution of altruism. We illustrate the utility of our approach using altruism, greenbeards, aggression, and weapons as examples. Our model provides a general predictive equation for the evolution of social phenotypes that encompasses specific cases such as kin selection and reciprocity. The parameters can be measured empirically, and we emphasize the importance of considering both IGEs and social selection, in addition to relatedness, when testing hypotheses about social evolution.",
    url = "https://doi.org/10.1111/j.1558-5646.2010.01012.x",
    doi = "10.1111/j.1558-5646.2010.01012.x",
    openalex = "W1806377556",
    references = "doi1010160022519364900384, doi1010160022519364900396, doi101086303168, doi101086406755, doi101093oso97801951223430010001, doi101111j14209101200801681x, doi101111j155856461983tb00236x, doi101126science7466396, doi101534genetics106062711, doi1023072529912, doi104159harvard9780674865327, doi105962bhltitle27468, goodnight1992contextual, openalexw2624262714"
}

Today's scientists are facing the enormous challenge of predicting how climate change will affect species distributions and species assemblages. To do so, ecologists are widely using phenomenological models of species distributions that mainly rely on the concept of species niche and generally ignore species' demography, species' adaptive potential, and biotic interactions. This review examines the potential role of the emerging synthetic discipline of evolutionary community ecology in improving our understanding of how climate change will alter future distribution of biodiversity. We review theoretical and empirical advances about the role of niche evolution, interspecific interactions, and their interplay in altering species geographic ranges and community assembly. We discuss potential ways to integrate complex feedbacks between ecology and evolution in ecological forecasting. We also point at a number of caveats in our understanding of the eco-evolutionary consequences of climate change and highlight several challenges for future research.

@article{doi101146annurevecolsys102209144628,
    author = "Lavergne, Sébastien and Mouquet, Nicolas and Thuiller, Wilfried and Ronce, Ophélie",
    title = "Biodiversity and Climate Change: Integrating Evolutionary and Ecological Responses of Species and Communities",
    year = "2010",
    journal = "Annual Review of Ecology Evolution and Systematics",
    abstract = "Today's scientists are facing the enormous challenge of predicting how climate change will affect species distributions and species assemblages. To do so, ecologists are widely using phenomenological models of species distributions that mainly rely on the concept of species niche and generally ignore species' demography, species' adaptive potential, and biotic interactions. This review examines the potential role of the emerging synthetic discipline of evolutionary community ecology in improving our understanding of how climate change will alter future distribution of biodiversity. We review theoretical and empirical advances about the role of niche evolution, interspecific interactions, and their interplay in altering species geographic ranges and community assembly. We discuss potential ways to integrate complex feedbacks between ecology and evolution in ecological forecasting. We also point at a number of caveats in our understanding of the eco-evolutionary consequences of climate change and highlight several challenges for future research.",
    url = "https://doi.org/10.1146/annurev-ecolsys-102209-144628",
    doi = "10.1146/annurev-ecolsys-102209-144628",
    openalex = "W2170175048",
    references = "doi101038nature01286, doi10108010635150802302427, doi101093oso97801985052350010001, doi101098rstb20090012, doi101111j13652435200701275x, doi101111j13652435200701278x, doi101111j13652435200701289x, doi101111j14610248200500792x, doi101111j14610248200500812x, doi101146annurevecolsys110308120159, doi101146annurevecolsys110308120317, doi101146annurevecolsys311343, doi101146annurevecolsys37091305110100, doi1015159780691206912, doi1023072260079, doi1023073071998, doi105860choice185809, doi105962bhltitle59991, openalexw2045291252, openalexw2151235472"
}

@article{doi101016jswevo201103001,
    author = "Zhou, Aimin and Qu, Boyang and Li, Hui and Zhao, Shi-Zheng and Suganthan, Ponnuthurai Nagaratnam and Zhang, Qingfu",
    title = "Multiobjective evolutionary algorithms: A survey of the state of the art",
    year = "2011",
    journal = "Swarm and Evolutionary Computation",
    url = "https://doi.org/10.1016/j.swevo.2011.03.001",
    doi = "10.1016/j.swevo.2011.03.001",
    openalex = "W2020320008",
    references = "doi101109tevc20092021467"
}

@article{doi101038nature09670,
    author = "Hoffmann, Ary A. and Sgrò, Carla M.",
    title = "Climate change and evolutionary adaptation",
    year = "2011",
    journal = "Nature",
    url = "https://doi.org/10.1038/nature09670",
    doi = "10.1038/nature09670",
    openalex = "W2008951609",
    references = "doi101038nature01286, doi101098rspb20081957, doi101111j1365294x200703428x, doi101111j14610248200801277x, doi101126science1070315, doi101146annurevecolsys110308120159, doi101146annurevecolsys271237"
}

The effect of ecological change on evolution has long been a focus of scientific research. The reverse--how evolutionary dynamics affect ecological traits--has only recently captured our attention, however, with the realization that evolution can occur over ecological time scales. This newly highlighted causal direction and the implied feedback loop--eco-evolutionary dynamics--is invigorating both ecologists and evolutionists and blurring the distinction between them. Despite some recent relevant studies, the importance of the evolution-to-ecology pathway across systems is still unknown. Only an extensive research effort involving multiple experimental approaches-particularly long-term field experiments--over a variety of ecological communities will provide the answer.

@article{doi101126science1193954,
    author = "Schoener, Thomas W.",
    title = "The Newest Synthesis: Understanding the Interplay of Evolutionary and Ecological Dynamics",
    year = "2011",
    journal = "Science",
    abstract = "The effect of ecological change on evolution has long been a focus of scientific research. The reverse--how evolutionary dynamics affect ecological traits--has only recently captured our attention, however, with the realization that evolution can occur over ecological time scales. This newly highlighted causal direction and the implied feedback loop--eco-evolutionary dynamics--is invigorating both ecologists and evolutionists and blurring the distinction between them. Despite some recent relevant studies, the importance of the evolution-to-ecology pathway across systems is still unknown. Only an extensive research effort involving multiple experimental approaches-particularly long-term field experiments--over a variety of ecological communities will provide the answer.",
    url = "https://doi.org/10.1126/science.1193954",
    doi = "10.1126/science.1193954",
    openalex = "W2083310645",
    references = "doi101007978940100585212, doi101016s0169534798013780, doi101038nature02430, doi101038nrg1877, doi101086282160, doi101086510633, doi101111j13652435200701275x, doi101111j13652435200701278x, doi101111j13652435200701289x, doi101111j14610248200500812x, doi101111j14610248200801179x, doi101111j155856461983tb00236x, doi101111j155856461999tb04550x, doi101126science2224620159, doi101146annurevecolsys31179, doi1023072408842, doi105860choice455580"
}

@article{doi101016jadvwatres201201005,
    author = "Reed, Patrick M. and Hadka, David and Herman, Jonathan D. and Kasprzyk, Joseph and Kollat, Joshua B.",
    title = "Evolutionary multiobjective optimization in water resources: The past, present, and future",
    year = "2012",
    journal = "Advances in Water Resources",
    url = "https://doi.org/10.1016/j.advwatres.2012.01.005",
    doi = "10.1016/j.advwatres.2012.01.005",
    openalex = "W2089678632",
    references = "doi101061ascewr194354520000053"
}

Whether biotic interactions limit geographic ranges has long been controversial, and traditional analyses of static distribution patterns have made little progress towards resolving this debate. Here, we use a novel phylogenetic approach to test whether biotic interactions constrain the transition to secondary sympatry following speciation. Applying this temporal framework to a diverse clade of passerine birds (Furnariidae), we reject models of geographic range overlap limited purely by dispersal or environmental constraints, and instead show that rates of secondary sympatry are positively associated with both the phylogenetic and morphological distance between species. Thus, transition rates to sympatry increase with time since divergence and accelerate as the ecological differences between species accumulate. Taken together, these results provide strong empirical evidence that biotic interactions - and primarily ecological competition - limit species distributions across large spatial and temporal scales. They also offer phylogenetic and trait-based metrics by which these interactions can be incorporated into ecological forecasting models.

@article{doi101111ele12043,
    author = "Pigot, Alex L. and Tobias, Joseph A.",
    title = "Species interactions constrain geographic range expansion over evolutionary time",
    year = "2012",
    journal = "Ecology Letters",
    abstract = "Whether biotic interactions limit geographic ranges has long been controversial, and traditional analyses of static distribution patterns have made little progress towards resolving this debate. Here, we use a novel phylogenetic approach to test whether biotic interactions constrain the transition to secondary sympatry following speciation. Applying this temporal framework to a diverse clade of passerine birds (Furnariidae), we reject models of geographic range overlap limited purely by dispersal or environmental constraints, and instead show that rates of secondary sympatry are positively associated with both the phylogenetic and morphological distance between species. Thus, transition rates to sympatry increase with time since divergence and accelerate as the ecological differences between species accumulate. Taken together, these results provide strong empirical evidence that biotic interactions - and primarily ecological competition - limit species distributions across large spatial and temporal scales. They also offer phylogenetic and trait-based metrics by which these interactions can be incorporated into ecological forecasting models.",
    url = "https://doi.org/10.1111/ele.12043",
    doi = "10.1111/ele.12043",
    openalex = "W2009833968",
    references = "doi101073pnas1014503108"
}

This study introduces the Borg multi-objective evolutionary algorithm (MOEA) for many-objective, multimodal optimization. The Borg MOEA combines ε-dominance, a measure of convergence speed named ε-progress, randomized restarts, and auto-adaptive multioperator recombination into a unified optimization framework. A comparative study on 33 instances of 18 test problems from the DTLZ, WFG, and CEC 2009 test suites demonstrates Borg meets or exceeds six state of the art MOEAs on the majority of the tested problems. The performance for each test problem is evaluated using a 1,000 point Latin hypercube sampling of each algorithm's feasible parameterization space. The statistical performance of every sampled MOEA parameterization is evaluated using 50 replicate random seed trials. The Borg MOEA is not a single algorithm; instead it represents a class of algorithms whose operators are adaptively selected based on the problem. The adaptive discovery of key operators is of particular importance for benchmarking how variation operators enhance search for complex many-objective problems.

@article{doi101162evcoa00075,
    author = "Hadka, David and Reed, Patrick M.",
    title = "Borg: An Auto-Adaptive Many-Objective Evolutionary Computing Framework",
    year = "2012",
    journal = "Evolutionary Computation",
    abstract = "This study introduces the Borg multi-objective evolutionary algorithm (MOEA) for many-objective, multimodal optimization. The Borg MOEA combines ε-dominance, a measure of convergence speed named ε-progress, randomized restarts, and auto-adaptive multioperator recombination into a unified optimization framework. A comparative study on 33 instances of 18 test problems from the DTLZ, WFG, and CEC 2009 test suites demonstrates Borg meets or exceeds six state of the art MOEAs on the majority of the tested problems. The performance for each test problem is evaluated using a 1,000 point Latin hypercube sampling of each algorithm's feasible parameterization space. The statistical performance of every sampled MOEA parameterization is evaluated using 50 replicate random seed trials. The Borg MOEA is not a single algorithm; instead it represents a class of algorithms whose operators are adaptively selected based on the problem. The adaptive discovery of key operators is of particular importance for benchmarking how variation operators enhance search for complex many-objective problems.",
    url = "https://doi.org/10.1162/evco\_a\_00075",
    doi = "10.1162/evco\_a\_00075",
    openalex = "W2167757882",
    references = "doi101061ascewr194354520000053"
}

@article{doi101038nrg3395,
    author = "Leinonen, Tuomas and McCairns, R. J. Scott and O’Hara, Robert B. and Merilä, Juha",
    title = "QST–FST comparisons: evolutionary and ecological insights from genomic heterogeneity",
    year = "2013",
    journal = "Nature Reviews Genetics",
    url = "https://doi.org/10.1038/nrg3395",
    doi = "10.1038/nrg3395",
    openalex = "W2018112412",
    references = "doi101073pnas0507648103, doi101111j14209101200701445x"
}

Having developed multiobjective optimization algorithms using evolutionary optimization methods and demonstrated their niche on various practical problems involving mostly two and three objectives, there is now a growing need for developing evolutionary multiobjective optimization (EMO) algorithms for handling many-objective (having four or more objectives) optimization problems. In this paper, we recognize a few recent efforts and discuss a number of viable directions for developing a potential EMO algorithm for solving many-objective optimization problems. Thereafter, we suggest a reference-point-based many-objective evolutionary algorithm following NSGA-II framework (we call it NSGA-III) that emphasizes population members that are nondominated, yet close to a set of supplied reference points. The proposed NSGA-III is applied to a number of many-objective test problems with three to 15 objectives and compared with two versions of a recently suggested EMO algorithm (MOEA/D). While each of the two MOEA/D methods works well on different classes of problems, the proposed NSGA-III is found to produce satisfactory results on all problems considered in this paper. This paper presents results on unconstrained problems, and the sequel paper considers constrained and other specialties in handling many-objective optimization problems.

@article{doi101109tevc20132281535,
    author = "Deb, Kalyanmoy and Jain, Himanshu",
    title = "An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints",
    year = "2013",
    journal = "IEEE Transactions on Evolutionary Computation",
    abstract = "Having developed multiobjective optimization algorithms using evolutionary optimization methods and demonstrated their niche on various practical problems involving mostly two and three objectives, there is now a growing need for developing evolutionary multiobjective optimization (EMO) algorithms for handling many-objective (having four or more objectives) optimization problems. In this paper, we recognize a few recent efforts and discuss a number of viable directions for developing a potential EMO algorithm for solving many-objective optimization problems. Thereafter, we suggest a reference-point-based many-objective evolutionary algorithm following NSGA-II framework (we call it NSGA-III) that emphasizes population members that are nondominated, yet close to a set of supplied reference points. The proposed NSGA-III is applied to a number of many-objective test problems with three to 15 objectives and compared with two versions of a recently suggested EMO algorithm (MOEA/D). While each of the two MOEA/D methods works well on different classes of problems, the proposed NSGA-III is found to produce satisfactory results on all problems considered in this paper. This paper presents results on unconstrained problems, and the sequel paper considers constrained and other specialties in handling many-objective optimization problems.",
    url = "https://doi.org/10.1109/tevc.2013.2281535",
    doi = "10.1109/tevc.2013.2281535",
    openalex = "W2022485595",
    references = "doi101109tevc20092021467, openalexw1595498733"
}

Evolutionary responses are required for tree populations to be able to track climate change. Results of 250 years of common garden experiments show that most forest trees have evolved local adaptation, as evidenced by the adaptive differentiation of populations in quantitative traits, reflecting environmental conditions of population origins. On the basis of the patterns of quantitative variation for 19 adaptation-related traits studied in 59 tree species (mostly temperate and boreal species from the Northern hemisphere), we found that genetic differentiation between populations and clinal variation along environmental gradients were very common (respectively, 90% and 78% of cases). Thus, responding to climate change will likely require that the quantitative traits of populations again match their environments. We examine what kind of information is needed for evaluating the potential to respond, and what information is already available. We review the genetic models related to selection responses, and what is known currently about the genetic basis of the traits. We address special problems to be found at the range margins, and highlight the need for more modeling to understand specific issues at southern and northern margins. We need new common garden experiments for less known species. For extensively studied species, new experiments are needed outside the current ranges. Improving genomic information will allow better prediction of responses. Competitive and other interactions within species and interactions between species deserve more consideration. Despite the long generation times, the strong background in quantitative genetics and growing genomic resources make forest trees useful species for climate change research. The greatest adaptive response is expected when populations are large, have high genetic variability, selection is strong, and there is ecological opportunity for establishment of better adapted genotypes.

@article{doi101111gcb12181,
    author = "Alberto, Florian and Aitken, Sally N. and Alı́a, Ricardo and González‐Martínez, Santiago C. and Hänninen, Heikki and Kremer, Antoine and Lefèvre, François and Lenormand, Thomas and Yeaman, Sam and Whetten, Ross and Savolainen, Outi",
    title = "Potential for evolutionary responses to climate change – evidence from tree populations",
    year = "2013",
    journal = "Global Change Biology",
    abstract = "Evolutionary responses are required for tree populations to be able to track climate change. Results of 250 years of common garden experiments show that most forest trees have evolved local adaptation, as evidenced by the adaptive differentiation of populations in quantitative traits, reflecting environmental conditions of population origins. On the basis of the patterns of quantitative variation for 19 adaptation-related traits studied in 59 tree species (mostly temperate and boreal species from the Northern hemisphere), we found that genetic differentiation between populations and clinal variation along environmental gradients were very common (respectively, 90\% and 78\% of cases). Thus, responding to climate change will likely require that the quantitative traits of populations again match their environments. We examine what kind of information is needed for evaluating the potential to respond, and what information is already available. We review the genetic models related to selection responses, and what is known currently about the genetic basis of the traits. We address special problems to be found at the range margins, and highlight the need for more modeling to understand specific issues at southern and northern margins. We need new common garden experiments for less known species. For extensively studied species, new experiments are needed outside the current ranges. Improving genomic information will allow better prediction of responses. Competitive and other interactions within species and interactions between species deserve more consideration. Despite the long generation times, the strong background in quantitative genetics and growing genomic resources make forest trees useful species for climate change research. The greatest adaptive response is expected when populations are large, have high genetic variability, selection is strong, and there is ecological opportunity for establishment of better adapted genotypes.",
    url = "https://doi.org/10.1111/gcb.12181",
    doi = "10.1111/gcb.12181",
    openalex = "W2152279961",
    references = "doi101111j00221112200400433x, doi101111j14209101200701445x"
}

@article{doi101016jenvsoft201409013,
    author = "Maier, Holger R. and Kapelan, Zoran and Kasprzyk, Joseph and Kollat, Joshua B. and Matott, L. Shawn and da Conceição Cunha, Maria and Dandy, Graeme C. and Gibbs, Matthew S. and Keedwell, Edward and Marchi, Angela and Ostfeld, Avi and Savić, Dragan and Solomatine, Dimitri and Vrugt, Jasper A. and Zecchin, Aaron C. and Minsker, Barbara and Barbour, Emily and Kuczera, G. and Pasha, Fayzul and Castelletti, Andrea and Giuliani, Matteo and Reed, Patrick M.",
    title = "Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions",
    year = "2014",
    journal = "Environmental Modelling \& Software",
    url = "https://doi.org/10.1016/j.envsoft.2014.09.013",
    doi = "10.1016/j.envsoft.2014.09.013",
    openalex = "W2108750696",
    references = "doi101016jpaerosci200502001, doi1010292011wr011527, doi101061ascewr194354520000053, doi10108003052159508941193"
}

Summary Understanding the dynamics of speciation, extinction and phenotypic evolution is a central challenge in evolutionary biology. Here, we present BAMM tools, an r package for the analysis and visualization of macroevolutionary dynamics on phylogenetic trees. BAMM tools is a companion package to BAMM, an open‐source program for reversible‐jump MCMC analyses of diversification and trait evolution. Functions in BAMM tools operate directly on output from the BAMM program. The package is oriented towards reconstructing and visualizing changes in evolutionary rates through time and across clades in a Bayesian statistical framework. BAMM tools enables users to extract credible sets of diversification shifts and to identify diversification histories with the maximum a posteriori probability. Users can compare the fit of alternative diversification models using Bayes factors and by directly comparing model posterior probabilities. By providing a robust framework for quantifying uncertainty in macroevolutionary dynamics, BAMM tools will facilitate inference on the complex mixture of processes that have shaped the distribution of species and phenotypes across the tree of life.

@article{doi1011112041210x12199,
    author = "Rabosky, Daniel L. and Gründler, Michael C. and Anderson, Carlos and Title, Pascal O. and Shi, Jeff J. and Brown, Joseph W. and Huang, Huateng and Larson, Joanna G.",
    title = "BAMM tools: an R package for the analysis of evolutionary dynamics on phylogenetic trees",
    year = "2014",
    journal = "Methods in Ecology and Evolution",
    abstract = "Summary Understanding the dynamics of speciation, extinction and phenotypic evolution is a central challenge in evolutionary biology. Here, we present BAMM tools, an r package for the analysis and visualization of macroevolutionary dynamics on phylogenetic trees. BAMM tools is a companion package to BAMM, an open‐source program for reversible‐jump MCMC analyses of diversification and trait evolution. Functions in BAMM tools operate directly on output from the BAMM program. The package is oriented towards reconstructing and visualizing changes in evolutionary rates through time and across clades in a Bayesian statistical framework. BAMM tools enables users to extract credible sets of diversification shifts and to identify diversification histories with the maximum a posteriori probability. Users can compare the fit of alternative diversification models using Bayes factors and by directly comparing model posterior probabilities. By providing a robust framework for quantifying uncertainty in macroevolutionary dynamics, BAMM tools will facilitate inference on the complex mixture of processes that have shaped the distribution of species and phenotypes across the tree of life.",
    url = "https://doi.org/10.1111/2041-210x.12199",
    doi = "10.1111/2041-210x.12199",
    openalex = "W1975222689",
    references = "doi101038ncomms2958, doi101111j15585646201001026x, doi101111j2041210x201100169x, doi101371journalpbio1001775, doi101371journalpone0089543"
}

Recently, a large number of multi-objective evolutionary algorithms (MOEAs) for many-objective optimization problems have been proposed in the evolutionary computation community. However, an exhaustive benchmarking study has never been performed. As a result, the performance of the MOEAs has not been well understood yet. Moreover, in almost all previous studies, the performance of the MOEAs was evaluated based on nondominated solutions in the final population at the end of the search. Such traditional benchmarking methodology has several critical issues. In this paper, we exhaustively investigate the anytime performance of 21 MOEAs using an unbounded external archive (UEA), which stores all nondominated solutions found during the search process. Each MOEA is evaluated under two optimization scenarios called UEA and reduced UEA in addition to the standard final population scenario. These two scenarios are more practical in real-world applications than the final population scenario. Experimental results obtained under the two scenarios are significantly different from the previously reported results under the final population scenario. For example, results on the Walking Fish Group test problems with up to six objectives indicate that some recently proposed MOEAs are outperformed by some classical MOEAs. We also analyze the reason why some classical MOEAs work well under the UEA and the reduced UEA scenarios.

@article{doi101109access20172751071,
    author = "Tanabe, Ryoji and Ishibuchi, Hisao and Oyama, Akira",
    title = "Benchmarking Multi- and Many-Objective Evolutionary Algorithms Under Two Optimization Scenarios",
    year = "2017",
    journal = "IEEE Access",
    abstract = "Recently, a large number of multi-objective evolutionary algorithms (MOEAs) for many-objective optimization problems have been proposed in the evolutionary computation community. However, an exhaustive benchmarking study has never been performed. As a result, the performance of the MOEAs has not been well understood yet. Moreover, in almost all previous studies, the performance of the MOEAs was evaluated based on nondominated solutions in the final population at the end of the search. Such traditional benchmarking methodology has several critical issues. In this paper, we exhaustively investigate the anytime performance of 21 MOEAs using an unbounded external archive (UEA), which stores all nondominated solutions found during the search process. Each MOEA is evaluated under two optimization scenarios called UEA and reduced UEA in addition to the standard final population scenario. These two scenarios are more practical in real-world applications than the final population scenario. Experimental results obtained under the two scenarios are significantly different from the previously reported results under the final population scenario. For example, results on the Walking Fish Group test problems with up to six objectives indicate that some recently proposed MOEAs are outperformed by some classical MOEAs. We also analyze the reason why some classical MOEAs work well under the UEA and the reduced UEA scenarios.",
    url = "https://doi.org/10.1109/access.2017.2751071",
    doi = "10.1109/access.2017.2751071",
    openalex = "W2753944351",
    references = "doi101016jeswa201610015"
}

The diversity in hydrologic models has historically led to great controversy on the "correct" approach to process-based hydrologic modeling, with debates centered on the adequacy of process parameterizations, data limitations and uncertainty, and computational constraints on model analysis. In this paper, we revisit key modeling challenges on requirements to (1) define suitable model equations, (2) define adequate model parameters, and (3) cope with limitations in computing power. We outline the historical modeling challenges, provide examples of modeling advances that address these challenges, and define outstanding research needs. We illustrate how modeling advances have been made by groups using models of different type and complexity, and we argue for the need to more effectively use our diversity of modeling approaches in order to advance our collective quest for physically realistic hydrologic models.

@article{doi105194hess2134272017,
    author = "Clark, Martyn and Bierkens, Marc F. P. and Samaniego, Luis and Woods, Ross and Uijlenhoet, R. and Bennett, Katrina E. and Pauwels, Valentijn and Cai, Xitian and Wood, Andrew W. and Peters‐Lidard, C. D.",
    title = "The evolution of process-based hydrologic models: historical challenges and the collective quest for physical realism",
    year = "2017",
    journal = "Hydrology and earth system sciences",
    abstract = {The diversity in hydrologic models has historically led to great controversy on the "correct" approach to process-based hydrologic modeling, with debates centered on the adequacy of process parameterizations, data limitations and uncertainty, and computational constraints on model analysis. In this paper, we revisit key modeling challenges on requirements to (1) define suitable model equations, (2) define adequate model parameters, and (3) cope with limitations in computing power. We outline the historical modeling challenges, provide examples of modeling advances that address these challenges, and define outstanding research needs. We illustrate how modeling advances have been made by groups using models of different type and complexity, and we argue for the need to more effectively use our diversity of modeling approaches in order to advance our collective quest for physically realistic hydrologic models.},
    url = "https://doi.org/10.5194/hess-21-3427-2017",
    doi = "10.5194/hess-21-3427-2017",
    openalex = "W2572622164",
    references = "doi1010022016jd025097, doi1010292011wr011527, doi101175jhmd1500541"
}

There have been few researches on solving multimodal multiobjective optimization problems, whereas they are commonly seen in real-world applications but difficult for the existing evolutionary optimizers. In this paper, we propose a novel multimodal multiobjective evolutionary algorithm using two-archive and recombination strategies. In the proposed algorithm, the properties of decision variables and the relationships among them are analyzed at first to guide the evolutionary search. Then, a general framework using two archives, i.e., the convergence and the diversity archives, is adopted to cooperatively solve these problems. Moreover, the diversity archive simultaneously employs a clustering strategy to guarantee diversity in the objective space and a niche-based clearing strategy to promote the same in the decision space. At the end of evolution process, solutions in the convergence and the diversity archives are recombined to obtain a large number of multiple Pareto optimal solutions. In addition, a set of benchmark test functions and a performance metric are designed for multimodal multiobjective optimization. The proposed algorithm is empirically compared with two state-of-the-art evolutionary algorithms on these test functions. The comparative results demonstrate that the overall performance of the proposed algorithm is significantly superior to the competing algorithms.

@article{doi101109tevc20182879406,
    author = "Liu, Yiping and Yen, Gary G. and Gong, Dunwei",
    title = "A Multimodal Multiobjective Evolutionary Algorithm Using Two-Archive and Recombination Strategies",
    year = "2018",
    journal = "IEEE Transactions on Evolutionary Computation",
    abstract = "There have been few researches on solving multimodal multiobjective optimization problems, whereas they are commonly seen in real-world applications but difficult for the existing evolutionary optimizers. In this paper, we propose a novel multimodal multiobjective evolutionary algorithm using two-archive and recombination strategies. In the proposed algorithm, the properties of decision variables and the relationships among them are analyzed at first to guide the evolutionary search. Then, a general framework using two archives, i.e., the convergence and the diversity archives, is adopted to cooperatively solve these problems. Moreover, the diversity archive simultaneously employs a clustering strategy to guarantee diversity in the objective space and a niche-based clearing strategy to promote the same in the decision space. At the end of evolution process, solutions in the convergence and the diversity archives are recombined to obtain a large number of multiple Pareto optimal solutions. In addition, a set of benchmark test functions and a performance metric are designed for multimodal multiobjective optimization. The proposed algorithm is empirically compared with two state-of-the-art evolutionary algorithms on these test functions. The comparative results demonstrate that the overall performance of the proposed algorithm is significantly superior to the competing algorithms.",
    url = "https://doi.org/10.1109/tevc.2018.2879406",
    doi = "10.1109/tevc.2018.2879406",
    openalex = "W2899519149",
    references = "doi101109tevc20092021467"
}

Vision-based control systems are key enablers of many autonomous vehicular systems, including self-driving cars. Testing such systems is complicated by complex and multidimensional input spaces. We propose an automated testing algorithm that builds on learnable evolutionary algorithms. These algorithms rely on machine learning or a combination of machine learning and Darwinian genetic operators to guide the generation of new solutions (test scenarios in our context). Our approach combines multiobjective population-based search algorithms and decision tree classification models to achieve the following goals: First, classification models guide the search-based generation of tests faster towards critical test scenarios (i.e., test scenarios leading to failures). Second, search algorithms refine classification models so that the models can accurately characterize critical regions (i.e., the regions of a test input space that are likely to contain most critical test scenarios). Our evaluation performed on an industrial automotive automotive system shows that: (1) Our algorithm outperforms a baseline evolutionary search algorithm and generates 78% more distinct, critical test scenarios compared to the baseline algorithm. (2) Our algorithm accurately characterizes critical regions of the system under test, thus identifying the conditions that are likely to lead to system failures.

@article{doi10114531801553180160,
    author = "Abdessalem, Raja Ben and Nejati, Shiva and Briand, Lionel and Stifter, Thomas",
    title = "Testing vision-based control systems using learnable evolutionary algorithms",
    year = "2018",
    abstract = "Vision-based control systems are key enablers of many autonomous vehicular systems, including self-driving cars. Testing such systems is complicated by complex and multidimensional input spaces. We propose an automated testing algorithm that builds on learnable evolutionary algorithms. These algorithms rely on machine learning or a combination of machine learning and Darwinian genetic operators to guide the generation of new solutions (test scenarios in our context). Our approach combines multiobjective population-based search algorithms and decision tree classification models to achieve the following goals: First, classification models guide the search-based generation of tests faster towards critical test scenarios (i.e., test scenarios leading to failures). Second, search algorithms refine classification models so that the models can accurately characterize critical regions (i.e., the regions of a test input space that are likely to contain most critical test scenarios). Our evaluation performed on an industrial automotive automotive system shows that: (1) Our algorithm outperforms a baseline evolutionary search algorithm and generates 78\% more distinct, critical test scenarios compared to the baseline algorithm. (2) Our algorithm accurately characterizes critical regions of the system under test, thus identifying the conditions that are likely to lead to system failures.",
    url = "https://doi.org/10.1145/3180155.3180160",
    doi = "10.1145/3180155.3180160",
    openalex = "W2775678306",
    references = "doi101002stvr294, doi10100797830301054641, doi101016c20090197155, doi101023a1007677805582, doi1011094235797969, doi1011094235996017, doi10114297898112019670001, doi1023072288003, doi105860choice505018, openalexw225560312, openalexw2966207845"
}

Basal or standard metabolic rate reflects the minimum amount of energy required to maintain body processes, while the maximum metabolic rate sets the ceiling for aerobic work. There is typically up to three-fold intraspecific variation in both minimal and maximal rates of metabolism, even after controlling for size, sex and age; these differences are consistent over time within a given context, but both minimal and maximal metabolic rates are plastic and can vary in response to changing environments. Here we explore the causes of intraspecific and phenotypic variation at the organ, tissue and mitochondrial levels. We highlight the growing evidence that individuals differ predictably in the flexibility of their metabolic rates and in the extent to which they can suppress minimal metabolism when food is limiting but increase the capacity for aerobic metabolism when a high work rate is beneficial. It is unclear why this intraspecific variation in metabolic flexibility persists-possibly because of trade-offs with the flexibility of other traits-but it has consequences for the ability of populations to respond to a changing world. It is clear that metabolic rates are targets of selection, but more research is needed on the fitness consequences of rates of metabolism and their plasticity at different life stages, especially in natural conditions. This article is part of the theme issue 'The role of plasticity in phenotypic adaptation to rapid environmental change'.

@article{doi101098rstb20180180,
    author = "Norin, Tommy and Metcalfe, Neil B.",
    title = "Ecological and evolutionary consequences of metabolic rate plasticity in response to environmental change",
    year = "2019",
    journal = "Philosophical Transactions of the Royal Society B Biological Sciences",
    abstract = "Basal or standard metabolic rate reflects the minimum amount of energy required to maintain body processes, while the maximum metabolic rate sets the ceiling for aerobic work. There is typically up to three-fold intraspecific variation in both minimal and maximal rates of metabolism, even after controlling for size, sex and age; these differences are consistent over time within a given context, but both minimal and maximal metabolic rates are plastic and can vary in response to changing environments. Here we explore the causes of intraspecific and phenotypic variation at the organ, tissue and mitochondrial levels. We highlight the growing evidence that individuals differ predictably in the flexibility of their metabolic rates and in the extent to which they can suppress minimal metabolism when food is limiting but increase the capacity for aerobic metabolism when a high work rate is beneficial. It is unclear why this intraspecific variation in metabolic flexibility persists-possibly because of trade-offs with the flexibility of other traits-but it has consequences for the ability of populations to respond to a changing world. It is clear that metabolic rates are targets of selection, but more research is needed on the fitness consequences of rates of metabolism and their plasticity at different life stages, especially in natural conditions. This article is part of the theme issue 'The role of plasticity in phenotypic adaptation to rapid environmental change'.",
    url = "https://doi.org/10.1098/rstb.2018.0180",
    doi = "10.1098/rstb.2018.0180",
    openalex = "W2913136619",
    references = "doi101038nature15256, doi101086649964"
}

Flexible job shop scheduling problems (FJSP) have received much attention from academia and industry for many years. Due to their exponential complexity, swarm intelligence (SI) and evolutionary algorithms (EA) are developed, employed and improved for solving them. More than 60% of the publications are related to SI and EA. This paper intents to give a comprehensive literature review of SI and EA for solving FJSP. First, the mathematical model of FJSP is presented and the constraints in applications are summarized. Then, the encoding and decoding strategies for connecting the problem and algorithms are reviewed. The strategies for initializing algorithms? population and local search operators for improving convergence performance are summarized. Next, one classical hybrid genetic algorithm (GA) and one newest imperialist competitive algorithm (ICA) with variables neighborhood search (VNS) for solving FJSP are presented. Finally, we summarize, discuss and analyze the status of SI and EA for solving FJSP and give insight into future research directions.

@article{doi101109jas20191911540,
    author = "Gao, Kaizhou and Cao, Zhiguang and Zhang, Le and Chen, Zhenghua and Han, Yuyan and Pan, Quan-Ke",
    title = "A review on swarm intelligence and evolutionary algorithms for solving flexible job shop scheduling problems",
    year = "2019",
    journal = "IEEE/CAA Journal of Automatica Sinica",
    abstract = "Flexible job shop scheduling problems (FJSP) have received much attention from academia and industry for many years. Due to their exponential complexity, swarm intelligence (SI) and evolutionary algorithms (EA) are developed, employed and improved for solving them. More than 60\% of the publications are related to SI and EA. This paper intents to give a comprehensive literature review of SI and EA for solving FJSP. First, the mathematical model of FJSP is presented and the constraints in applications are summarized. Then, the encoding and decoding strategies for connecting the problem and algorithms are reviewed. The strategies for initializing algorithms? population and local search operators for improving convergence performance are summarized. Next, one classical hybrid genetic algorithm (GA) and one newest imperialist competitive algorithm (ICA) with variables neighborhood search (VNS) for solving FJSP are presented. Finally, we summarize, discuss and analyze the status of SI and EA for solving FJSP and give insight into future research directions.",
    url = "https://doi.org/10.1109/jas.2019.1911540",
    doi = "10.1109/jas.2019.1911540",
    openalex = "W2951307174",
    references = "doi101016jasoc200910006"
}

Multimodal multiobjective optimization aims to find all Pareto optimal solutions, including overlapping solutions in the objective space. Multimodal multiobjective optimization has been investigated in the evolutionary computation community since 2005. However, it is difficult to survey existing studies in this field because they have been independently conducted and do not explicitly use the term “multimodal multiobjective optimization.” To address this issue, this letter reviews the existing studies of evolutionary multimodal multiobjective optimization, including studies published under names that are different from multimodal multiobjective optimization. Our review also clarifies open issues in this research area.

@article{doi101109tevc20192909744,
    author = "Tanabe, Ryoji and Ishibuchi, Hisao",
    title = "A Review of Evolutionary Multimodal Multiobjective Optimization",
    year = "2019",
    journal = "IEEE Transactions on Evolutionary Computation",
    abstract = "Multimodal multiobjective optimization aims to find all Pareto optimal solutions, including overlapping solutions in the objective space. Multimodal multiobjective optimization has been investigated in the evolutionary computation community since 2005. However, it is difficult to survey existing studies in this field because they have been independently conducted and do not explicitly use the term “multimodal multiobjective optimization.” To address this issue, this letter reviews the existing studies of evolutionary multimodal multiobjective optimization, including studies published under names that are different from multimodal multiobjective optimization. Our review also clarifies open issues in this research area.",
    url = "https://doi.org/10.1109/tevc.2019.2909744",
    doi = "10.1109/tevc.2019.2909744",
    openalex = "W2947446743",
    references = "doi101109tevc20092021467"
}

Mountain regions are unusually biodiverse, with rich aggregations of small-ranged species that form centers of endemism. Mountains play an array of roles for Earth's biodiversity and affect neighboring lowlands through biotic interchange, changes in regional climate, and nutrient runoff. The high biodiversity of certain mountains reflects the interplay of multiple evolutionary mechanisms: enhanced speciation rates with distinct opportunities for coexistence and persistence of lineages, shaped by long-term climatic changes interacting with topographically dynamic landscapes. High diversity in most tropical mountains is tightly linked to bedrock geology-notably, areas comprising mafic and ultramafic lithologies, rock types rich in magnesium and poor in phosphate that present special requirements for plant physiology. Mountain biodiversity bears the signature of deep-time evolutionary and ecological processes, a history well worth preserving.

@article{doi101126scienceaax0151,
    author = "Rahbek, Carsten and Borregaard, Michael K. and Antonelli, Alexandre and Colwell, Robert K. and Holt, Ben G. and Nogués‐Bravo, David and Rasmussen, Christian M. Ø. and Richardson, Katherine and Rosing, Minik T. and Whittaker, Robert J. and Fjeldså, Jon",
    title = "Building mountain biodiversity: Geological and evolutionary processes",
    year = "2019",
    journal = "Science",
    abstract = "Mountain regions are unusually biodiverse, with rich aggregations of small-ranged species that form centers of endemism. Mountains play an array of roles for Earth's biodiversity and affect neighboring lowlands through biotic interchange, changes in regional climate, and nutrient runoff. The high biodiversity of certain mountains reflects the interplay of multiple evolutionary mechanisms: enhanced speciation rates with distinct opportunities for coexistence and persistence of lineages, shaped by long-term climatic changes interacting with topographically dynamic landscapes. High diversity in most tropical mountains is tightly linked to bedrock geology-notably, areas comprising mafic and ultramafic lithologies, rock types rich in magnesium and poor in phosphate that present special requirements for plant physiology. Mountain biodiversity bears the signature of deep-time evolutionary and ecological processes, a history well worth preserving.",
    url = "https://doi.org/10.1126/science.aax0151",
    doi = "10.1126/science.aax0151",
    openalex = "W2973050715",
    references = "doi101038s415610180236z, doi101073pnas1813206116, doi101126science1228282, doi101126scienceaar5452"
}

Evolutionary algorithms have been shown to be very successful in solving multi-objective optimization problems (MOPs). However, their performance often deteriorates when solving MOPs with irregular Pareto fronts. To remedy this issue, a large body of research has been performed in recent years and many new algorithms have been proposed. This paper provides a comprehensive survey of the research on MOPs with irregular Pareto fronts. We start with a brief introduction to the basic concepts, followed by a summary of the benchmark test problems with irregular problems, an analysis of the causes of the irregularity, and real-world optimization problems with irregular Pareto fronts. Then, a taxonomy of the existing methodologies for handling irregular problems is given and representative algorithms are reviewed with a discussion of their strengths and weaknesses. Finally, open challenges are pointed out and a few promising future directions are suggested.

@article{doi101109jas20211003817,
    author = "Hua, Yicun and Liu, Qiqi and Hao, Kuangrong and Jin, Yaochu",
    title = "A Survey of Evolutionary Algorithms for Multi-Objective Optimization Problems With Irregular Pareto Fronts",
    year = "2021",
    journal = "IEEE/CAA Journal of Automatica Sinica",
    abstract = "Evolutionary algorithms have been shown to be very successful in solving multi-objective optimization problems (MOPs). However, their performance often deteriorates when solving MOPs with irregular Pareto fronts. To remedy this issue, a large body of research has been performed in recent years and many new algorithms have been proposed. This paper provides a comprehensive survey of the research on MOPs with irregular Pareto fronts. We start with a brief introduction to the basic concepts, followed by a summary of the benchmark test problems with irregular problems, an analysis of the causes of the irregularity, and real-world optimization problems with irregular Pareto fronts. Then, a taxonomy of the existing methodologies for handling irregular problems is given and representative algorithms are reviewed with a discussion of their strengths and weaknesses. Finally, open challenges are pointed out and a few promising future directions are suggested.",
    url = "https://doi.org/10.1109/jas.2021.1003817",
    doi = "10.1109/jas.2021.1003817",
    openalex = "W3120225493",
    references = "doi101109tevc20092021467"
}

Multimodal multiobjective problems (MMOPs) arise frequently in the real world, in which multiple Pareto-optimal solution (PS) sets correspond to the same point on the Pareto front. Traditional multiobjective evolutionary algorithms (MOEAs) show poor performance in solving MMOPs due to a lack of diversity maintenance in the decision space. Thus, recently, many multimodal MOEAs (MMEAs) have been proposed. However, for most existing MMEAs, the convergence performance in the objective space does not meet expectations. In addition, many of them cannot always obtain all equivalent Pareto solution sets. To address these issues, this study proposes an MMEA based on a weighted indicator, termed MMEA-WI. The algorithm integrates the diversity information of solutions in the decision space into an objective space performance indicator to maintain the diversity in the decision space and introduces a convergence archive to ensure a more effective approximation of the Pareto-optimal front (PF). These strategies can readily be applied to other indicator-based MOEAs. The experimental results show that MMEA-WI outperforms some state-of-the-art MMEAs on the chosen benchmark problems in terms of the inverted generational distance (IGD) and IGD in the decision space (IGDX) metrics.

@article{doi101109tevc20213078441,
    author = "Li, Wenhua and Zhang, Tao and Wang, Rui and Ishibuchi, Hisao",
    title = "Weighted Indicator-Based Evolutionary Algorithm for Multimodal Multiobjective Optimization",
    year = "2021",
    journal = "IEEE Transactions on Evolutionary Computation",
    abstract = "Multimodal multiobjective problems (MMOPs) arise frequently in the real world, in which multiple Pareto-optimal solution (PS) sets correspond to the same point on the Pareto front. Traditional multiobjective evolutionary algorithms (MOEAs) show poor performance in solving MMOPs due to a lack of diversity maintenance in the decision space. Thus, recently, many multimodal MOEAs (MMEAs) have been proposed. However, for most existing MMEAs, the convergence performance in the objective space does not meet expectations. In addition, many of them cannot always obtain all equivalent Pareto solution sets. To address these issues, this study proposes an MMEA based on a weighted indicator, termed MMEA-WI. The algorithm integrates the diversity information of solutions in the decision space into an objective space performance indicator to maintain the diversity in the decision space and introduces a convergence archive to ensure a more effective approximation of the Pareto-optimal front (PF). These strategies can readily be applied to other indicator-based MOEAs. The experimental results show that MMEA-WI outperforms some state-of-the-art MMEAs on the chosen benchmark problems in terms of the inverted generational distance (IGD) and IGD in the decision space (IGDX) metrics.",
    url = "https://doi.org/10.1109/tevc.2021.3078441",
    doi = "10.1109/tevc.2021.3078441",
    openalex = "W3160947956",
    references = "doi101109tevc20092021467"
}

Recent decades have witnessed great advancements in multiobjective evolutionary algorithms (MOEAs) for multiobjective optimization problems (MOPs). However, these progressively improved MOEAs have not necessarily been equipped with scalable and learnable problem-solving strategies for new and grand challenges brought by the scaling-up MOPs with continuously increasing complexity from diverse aspects, mainly, including expensive cost of function evaluations, many objectives, large-scale search space, time-varying environments, and multitask. Under different scenarios, divergent thinking is required in designing new powerful MOEAs for solving them effectively. In this context, research studies on learnable MOEAs with machine learning techniques have received extensive attention in the field of evolutionary computation. This article begins with a general taxonomy of scaling-up MOPs and learnable MOEAs, followed by an analysis of the challenges that these MOPs pose to traditional MOEAs. Then, we synthetically overview recent advances of learnable MOEAs in solving various scaling-up MOPs, focusing primarily on four attractive directions (i.e., learnable evolutionary discriminators for environmental selection, learnable evolutionary generators for reproduction, learnable evolutionary evaluators for function evaluations, and learnable evolutionary transfer modules for sharing or reusing optimization experience). The insight of learnable MOEAs is offered to readers as a reference to the general track of the efforts in this field.

@article{doi101109tevc20233250350,
    author = "Liu, Songbai and Lin, Qiuzhen and Li, Jianqiang and Tan, Kay Chen",
    title = "A Survey on Learnable Evolutionary Algorithms for Scalable Multiobjective Optimization",
    year = "2023",
    journal = "IEEE Transactions on Evolutionary Computation",
    abstract = "Recent decades have witnessed great advancements in multiobjective evolutionary algorithms (MOEAs) for multiobjective optimization problems (MOPs). However, these progressively improved MOEAs have not necessarily been equipped with scalable and learnable problem-solving strategies for new and grand challenges brought by the scaling-up MOPs with continuously increasing complexity from diverse aspects, mainly, including expensive cost of function evaluations, many objectives, large-scale search space, time-varying environments, and multitask. Under different scenarios, divergent thinking is required in designing new powerful MOEAs for solving them effectively. In this context, research studies on learnable MOEAs with machine learning techniques have received extensive attention in the field of evolutionary computation. This article begins with a general taxonomy of scaling-up MOPs and learnable MOEAs, followed by an analysis of the challenges that these MOPs pose to traditional MOEAs. Then, we synthetically overview recent advances of learnable MOEAs in solving various scaling-up MOPs, focusing primarily on four attractive directions (i.e., learnable evolutionary discriminators for environmental selection, learnable evolutionary generators for reproduction, learnable evolutionary evaluators for function evaluations, and learnable evolutionary transfer modules for sharing or reusing optimization experience). The insight of learnable MOEAs is offered to readers as a reference to the general track of the efforts in this field.",
    url = "https://doi.org/10.1109/tevc.2023.3250350",
    doi = "10.1109/tevc.2023.3250350",
    openalex = "W4322706826",
    references = "doi101016jeswa201610015"
}

This paper extends the Epic Cognition Theory (ECT) by examining clinical and anthropological evidence of how the mind, through symbolic narratives and "dream-immersion" processes, directly influences the physical body. We argue that humans are not merely products of their genetic code; they are active "narrative architects" who redesign their own biology by processing environmental and psychological stressors. We propose that phenomena traditionally viewed as purely psychological or pathological—such as the placebo and nocebo effects, therianthropy, conversion disorder, religious stigmata, hypnosis-induced physical changes, mass psychogenic illness, and psychogenic purpura (Gardner-Diamond syndrome)—are striking examples of this internal redesign process. These cases demonstrate how a symbolic identity, when internalized, can manipulate physiological and morphological responses. Novel hypotheses, including biophoton-mediated neuroplasticity, expand ECT's scope by proposing a biophysical mechanism for these epigenetic and cultural adaptations. By defining the Schizoid state as a mechanism of evolutionary metamorphosis in humans, this work frames Art not merely as an aesthetic pursuit, but as a vital instrument for “controlled Schizoid processing." We argue that by achieving awareness of their symbolic potential and expressing it through artistic agency, individuals can consciously master the mind-body redesign mechanism, effectively amplifying the placebo effect and neutralizing the destructive consequences of externally imposed narratives. This framework redefines illness, healing, and identity formation as processes at the intersection of physical and symbolic evolution, revealing the profound applicability of ECT in the fields of evolutionary biology, neuroanthropology, and narrative medicine.

@misc{büyük2026from,
    author = "Büyük, Sedat",
    title = "From Imagination to Biology: ECT's Case Studies of Mind-Body Evolutionary Redesign",
    year = "2026",
    publisher = "Zenodo",
    abstract = {This paper extends the Epic Cognition Theory (ECT) by examining clinical and anthropological evidence of how the mind, through symbolic narratives and "dream-immersion" processes, directly influences the physical body. We argue that humans are not merely products of their genetic code; they are active "narrative architects" who redesign their own biology by processing environmental and psychological stressors. We propose that phenomena traditionally viewed as purely psychological or pathological—such as the placebo and nocebo effects, therianthropy, conversion disorder, religious stigmata, hypnosis-induced physical changes, mass psychogenic illness, and psychogenic purpura (Gardner-Diamond syndrome)—are striking examples of this internal redesign process. These cases demonstrate how a symbolic identity, when internalized, can manipulate physiological and morphological responses. Novel hypotheses, including biophoton-mediated neuroplasticity, expand ECT's scope by proposing a biophysical mechanism for these epigenetic and cultural adaptations. By defining the Schizoid state as a mechanism of evolutionary metamorphosis in humans, this work frames Art not merely as an aesthetic pursuit, but as a vital instrument for “controlled Schizoid processing." We argue that by achieving awareness of their symbolic potential and expressing it through artistic agency, individuals can consciously master the mind-body redesign mechanism, effectively amplifying the placebo effect and neutralizing the destructive consequences of externally imposed narratives. This framework redefines illness, healing, and identity formation as processes at the intersection of physical and symbolic evolution, revealing the profound applicability of ECT in the fields of evolutionary biology, neuroanthropology, and narrative medicine.},
    url = "https://zenodo.org/doi/10.5281/zenodo.17148219",
    doi = "10.5281/zenodo.17148219"
}