Exploiting synergies of multiple crossovers: initial studies
Inki Hong, Andrew B. Kahng, Byung Ro Moon · 2002
Genetic algorithms (GAs) are believed to exploit the synergy between different traversals of the solution space that are afforded by crossover and mutation operators. While dozens of different crossovers are known, comparatively little attention has been devoted to improving performance by using multiple crossover operators within a given GA implementation. Here, we examine various aspects of combining different crossovers; we demonstrate that mixtures of crossovers can outperform any single crossover, and that choosing appropriate mixing proportions is critical for good performance. We conjecture that good crossover mixtures are characterized by "balance" in the crossovers' respective influences in the population, and explore three adaptive strategies for mixing crossovers. 1 Introduction The crossover and mutation operators of genetic algorithms (GAs) navigate the solution space in synergetic ways: crossover generates new solutions by combining traits of already-visited solutions, w...