论文信息 - Empirical Observations on the Roles of Crossover and Mutation

Empirical Observations on the Roles of Crossover and Mutation

There is a great deal of information to be gained from studying the details within a GA run. This paper investigates the roles of crossover and mutation by observing the actions and eeects of individual occurrences of each genetic operation. The observations are compared with some of the common expectations of these operators.

[1] Peter J. Angeline,et al. Explicitly Defined Introns and Destructive Crossover in Genetic Programming , 1996 .

[2] Larry J. Eshelman,et al. On Crossover as an Evolutionarily Viable Strategy , 1991, ICGA.

[3] W. Spears,et al. On the Virtues of Parameterized Uniform Crossover , 1995 .

[4] William M. Spears,et al. Crossover or Mutation? , 1992, FOGA.

[5] James R. Levenick. Inserting Introns Improves Genetic Algorithm Success Rate: Taking a Cue from Biology , 1991, ICGA.

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

[7] Larry J. Eshelman,et al. Crossover's Niche , 1993, ICGA.

[8] Terry Jones,et al. A Description of Holland's Royal Road Function , 1994, Evolutionary Computation.

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

[10] Melanie Mitchell,et al. The royal road for genetic algorithms: Fitness landscapes and GA performance , 1991 .

[11] Thomas C. Peachey,et al. The Nature of Mutation in Genetic Algorithms , 1995, ICGA.

[12] Dirk Thierens,et al. Mixing in Genetic Algorithms , 1993, ICGA.

[13] Larry J. Eshelman,et al. Biases in the Crossover Landscape , 1989, ICGA.