论文信息 - Competitive Evolution: A Natural Approach to Operator Selection

Competitive Evolution: A Natural Approach to Operator Selection

One of the main problems in applying evolutionary optimisation methods is the choice of operators and parameter values. This paper propose a competitive evolution method, in which several subpopulations are allowed to compete for computer time. The population with the fittest members, and that with the highest improvement rate in the recent past, are rewarded.

Q. Tuan Pham | Q. Pham

[1] Zbigniew Michalewicz,et al. An Experimental Comparison of Binary and Floating Point Representations in Genetic Algorithms , 1991, ICGA.

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

[3] Zbigniew Michalewicz,et al. Genetic Algorithms + Data Structures = Evolution Programs , 1992, Artificial Intelligence.

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

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

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

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

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

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

[10] Günter Rudolph,et al. Convergence analysis of canonical genetic algorithms , 1994, IEEE Trans. Neural Networks.

[11] Lawrence Davis,et al. Adapting Operator Probabilities in Genetic Algorithms , 1989, ICGA.

[12] H. H. Rosenbrock,et al. An Automatic Method for Finding the Greatest or Least Value of a Function , 1960, Comput. J..