论文信息 - Variable period adaptive genetic algorithm

Variable period adaptive genetic algorithm

Self-adapting genetic algorithm has two main factors contributing to its improved performance. The first is the effect of the progress of the evolution process where the fitness of the population improves as the number of generation increases. The second is the improvement due to the choice of the probabilities for the various genetic operators. In this paper, we propose a scheme that isolates the contributions of these two factors through the introduction of two competing populations. These two concurrent populations provide the necessary feedback to either prolong the duration of a good choice of the parameter setting or shorten that of a poor choice. Results from several numerical experiments have shown that the proposed scheme provides favorable performance over existing methods.

[1] Lawrence. Davis,et al. Handbook Of Genetic Algorithms , 1990 .

[2] William E. Hart,et al. Optimizing an Arbitrary Function is Hard for the Genetic Algorithm , 1991 .

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

[4] Nostrand Reinhold,et al. the utility of using the genetic algorithm approach on the problem of Davis, L. (1991), Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York. , 1991 .

[5] L. Darrell Whitley,et al. Serial and Parallel Genetic Algorithms as Function Optimizers , 1993, ICGA.

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

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

[8] Hans-Paul Schwefel,et al. Parallel Problem Solving from Nature — PPSN IV , 1996, Lecture Notes in Computer Science.

[9] Zbigniew Michalewicz,et al. Handbook of Evolutionary Computation , 1997 .

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

[11] M. E. Johnson,et al. Generalized simulated annealing for function optimization , 1986 .

[12] Peter Ross,et al. Cost Based Operator Rate Adaption: An Investigation , 1996, PPSN.

[13] Zbigniew Michalewicz,et al. Evolutionary Planner/Navigator: operator performance and self-tuning , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.