Benchmarking a (μ +λ ) Genetic Algorithm with Configurable Crossover Probability.

We investigate a family of \((\mu +\lambda )\) Genetic Algorithms (GAs) which creates offspring either from mutation or by recombining two randomly chosen parents. By scaling the crossover probability, we can thus interpolate from a fully mutation-only algorithm towards a fully crossover-based GA. We analyze, by empirical means, how the performance depends on the interplay of population size and the crossover probability.