Patricia Ortegon-Cano
ABSTRACT
Crossover is the most complicated of the standard genetic operators as well as one of the main operators in genetic algorithms. The role of the crossover operator has not been satisfactorily explained. It has not been easy to show if it is essential for the construction and exploitation of building blocks during evolution. In this paper we introduce an adaptive crossover operator for two test functions. The results show that there is a clear increase in the rate of evolution when information on the state of the population is used to select the crossover point, compared to random selection of the crossover point.
- D. E. Goldberg. Genetic Algorithms in Search. Optimization and Machine Learning. Addison-Wesley. Boston, MA, USA, 1989. Google Scholar
Digital Library
- J. H. Holland. Adaptation in Natural and Artificia. Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. The MIT Press, 1992. Google ScholarDigital Library
- R. Poli. Exact schema theory for genetic programming and variable-length genetic algorithms with one-point crossover. Genetic Programming and Evolvable Machines, 2(2):123--163, 2001. Google ScholarDigital Library
Index Terms
- Why recombination should be adaptive
Recommendations
Radical epistasis and the genotype-phenotype relationship
Models of evolution often assume that the offspring of two genotypes, which are genetically intermediate by definition, are also phenotypically intermediate. The continuity between genotype and phenotype interferes with the process of evolution on ...
Depictions of genotypic space for evaluating the suitability of different recombination operators
GECCO '12: Proceedings of the 14th annual conference on Genetic and evolutionary computationWhen the genetic algorithm recombines two parent genotypes, the differences between them define a genotypic subspace, and any offspring produced should be confined to this subspace. Although this might seem insignificant, those recombination (or ...
A novel sexual adaptive genetic algorithm based on two-step evolutionary scenario of baldwin effect and analysis of global convergence
GEC '09: Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary ComputationThis work presents a novel sexual adaptive genetic algorithm (NSAGA) based on two-step evolutionary scenario of Baldwin effect to overcome the shortcomings of traditional genetic algorithms, such as premature convergence, stochastic roaming, and poor ...
Comments