On benchmarking functions for genetic algorithms 论文
2001International Journal of Computer Mathematics引用 420
Metaheuristic Optimization Algorithms ResearchAdvanced Multi-Objective Optimization AlgorithmsEvolutionary Algorithms and Applications
摘要
Abstract This paper presents experimental results on the major benchmarking functions used for performance evaluation of Genetic Algorithms (GAs). Parameters considered include the effect of population size, crossover probability, mutation rate and pseudorandom generator. The general computational behavior of two basic GAs models, the Generational Replacement Model (GRM) and the Steady State Replacement Model (SSRM) is evaluated. Keywords: Genetic algorithmsBenchmarking functionsPopulation sizeMutation ratePseudo-random number generationC.R. Categories:: I.2.8F.2.2 * jason@uom.gr * jason@uom.gr Notes * jason@uom.gr