The paper is in three parts. First, we use simple adversary arguments to redevelop and explore some of the no-free-lunch (NFL) theorems and perhaps extend them a little. Second, we clarify the relationship of NFL theorems to algorithm theory and complexity classes such as NP. We claim that NFL is weaker in the sense that the constraints implied by the conjectures of traditional algorithm theory on what an evolutionary algorithm may be expected to accomplish are far more severe than those implied by NFL. Third, we take a brief look at how natural evolution relates to computation and optimization. We suggest that the evolution of complex systems exhibiting high degrees of orderliness is not equivalent in difficulty to optimizing hard (in the complexity sense) problems, and that the optimism in genetic algorithms (GAs) as universal optimizers is not justified by natural evolution.
This is an informal tutorial paper—most of the information presented is not formally proven, and is either “common knowledge” or formally proven elsewhere. Some of the claims are intuitions based on experience with algorithms, and in a more formal setting should be classified as conjectures.
Supported by Natural Sciences and Engineering Research Council Grant No. OGP8053. Department of Computing Science, University of Alberta, Edmonton, Alherta, Canada, T6G 2H1. email:firstname.lastname@example.org