On the Dynamics of EAs without Selection

This paper investigates the dynamics of evolutionary algorithms (EAs) without tness based selection (constant tness). Such algorithms exhibit a behavior similar to the MISR eeect (mutation-induced speciation by recombination) which has been found in the analysis of (== D ;) evolution strategies. It will be shown that this behavior can be observed in a variety of EAs, not only in unrestricted search spaces, but also in binary GAs. The quantiication of this eeect is done by introducing the expected population variance 2 P. The evolution of 2 P over the time g is analytically calculated for both unrestricted and binary search spaces. The theoretical predictions are compared with experiments. The genetic drift phenomenon and the diiusion eeect are derived from the general 2 P formulae, and it will be shown that MISR is a nite population size sampling eeect which cannot be observed in innnite populations.

[1]  Sys,et al.  How Gas Do Not Work Understanding Gas without Schemata and Building Blocks , 1995 .

[2]  K. Dejong,et al.  An analysis of the behavior of a class of genetic adaptive systems , 1975 .

[3]  Kennetb A. De Genetic Algorithms Are NOT Function Optimizers , 1992 .

[4]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[5]  Heinz Mühlenbein,et al.  On the Mean Convergence Time of Evolutionary Algorithms without Selection and Mutation , 1994, PPSN.

[6]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[7]  Hans-Georg Beyer,et al.  Toward a Theory of Evolution Strategies: On the Benefits of Sex the (/, ) Theory , 1995, Evolutionary Computation.

[8]  Heinz Mühlenbein,et al.  The Science of Breeding and Its Application to the Breeder Genetic Algorithm (BGA) , 1993, Evolutionary Computation.

[9]  H. Beyer An alternative explanation for the manner in which genetic algorithms operate. , 1997, Bio Systems.

[10]  John J. Grefenstette,et al.  Conditions for Implicit Parallelism , 1990, FOGA.

[11]  Gilbert Syswerda,et al.  Uniform Crossover in Genetic Algorithms , 1989, ICGA.

[12]  J. Thoday Population Genetics , 1956, Nature.

[13]  BeyerHans-Georg Toward a theory of evolution strategies , 1993 .

[14]  David E. Goldberg,et al.  Finite Markov Chain Analysis of Genetic Algorithms , 1987, ICGA.