Dembski, William A., 2002b. *No Free Lunch*, Lanham, MD: Rowman &
Littlefield, pp. xii, 199-212.

- The NFL theorems do not apply to biological evolution. The NFL
theorems apply only when the fitness function is independent of the
algorithm, but in evolution, evolving populations affect the
environment and each other and therefore the fitness functions.

It should also be noted that the NFL theorems do not refer to finding a target. They can apply to problems such as finding which of several algorithms performs best; such application differs from Dembski's concept of a target.

Dembski himself later wrote that the NFL theorems are not important to his point, which is about displacement and conservation of information (Dembski 2002a). - The NFL theorems consider the average of all fitness functions.
Finding an above-average fitness function is not complicated and is
often trivial. If you want a solution that performs well according to
some metric, then a fitness function that measures that metric will
usually work better than blind search. For example, if you are
interested in survival and reproduction in a certain environment, then
survival and reproduction in that environment is a good choice for a
fitness function.
- The ultimate test of a concept is whether it works. Evolutionary
algorithms work. They find solutions to many problems that are
intractable with other methods. If mathematics contradicts reliable
observation, the math is misapplied, irrelevant, or wrong.
- Complex specified information does not signify
design.
- No design theorist has ever shown that complex specified information
exists in life.
- That evolution uses information from the environment (via the fitness function) is nothing new. The process is called adaptation. Darwin wrote something about the general subject (Darwin 1859). It does not imply design.

- Darwin, C., 1872.
*The Origin of Species*, 1st Edition. Senate, London. http://www.talkorigins.org/faqs/origin.html - Dembski, William A., 2002a (Nov. 6). The ARN Design Forum: What genetic algorithms can do. http://www.arn.org/ubb/ultimatebb.php?ubb=get_topic;f=13;t=000428;p=1
- Wolpert, D. H. and W. G. Macready, 1997. No Free Lunch theorems for
optimization.
*IEEE Transactions on Evolutionary Computation*1(1): 67-82. http://citeseer.nj.nec.com/wolpert96no.html

Wolpert, David, 2002. William Dembski's treatment of the No Free Lunch theorems is written in jello.

created 2003-10-22, modified 2003-10-25