Exploiting synergies of multiple crossovers: initial studies

Genetic algorithms (GAS) are believed to exploit the synergy between diKerent 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.