Evolving Mutation Rates for the Self-Optimisation of Genetic Algorithms

A version of the standard genetic algorithm, in which the mutation rate is allowed to evolve freely, is applied across a set of optimisation problems. The resulting dynamics confirm the hypothesis that mutation rate, when allowed to evolve, will do so partly as a function of altitude in the fitness landscape. Further, it is demonstrated that this fact can be exploited in order to improve efficiency of the genetic algorithm when applied to a particular class of optimisation problem. Specifically, significant efficiency gains are established in those problems in which the fitness function is not stationary over time.

[1]  Zbigniew Michalewicz,et al.  Parameter control in evolutionary algorithms , 1999, IEEE Trans. Evol. Comput..

[2]  Kenneth Alan De Jong,et al.  An analysis of the behavior of a class of genetic adaptive systems. , 1975 .

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

[4]  Lawrence Davis,et al.  Genetic Algorithms and Simulated Annealing , 1987 .

[5]  Rajarshi Das,et al.  A Study of Control Parameters Affecting Online Performance of Genetic Algorithms for Function Optimization , 1989, ICGA.

[6]  F. Taddei,et al.  Role of mutator alleles in adaptive evolution , 1997, Nature.

[7]  R. Lenski,et al.  Evolution of high mutation rates in experimental populations of E. coli , 1997, Nature.

[8]  Zbigniew Michalewicz,et al.  Adaptation in evolutionary computation: a survey , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

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

[10]  J. Drake.  A constant rate of spontaneous mutation in DNA-based microbes. , 1991, Proceedings of the National Academy of Sciences of the United States of America.

[11]  John J. Grefenstette,et al.  Optimization of Control Parameters for Genetic Algorithms , 1986, IEEE Transactions on Systems, Man, and Cybernetics.

[12]  J. David Schaffer,et al.  Proceedings of the third international conference on Genetic algorithms , 1989 .