Crossover, Macromutation, and Population-Based Search

A Genetic Algorithm (GA) maintains a population of individuals for the express purpose of improving performance via communication of information between contemporary individuals. This is achieved in a GA through the use of a crossover operator. If crossover is not a useful method for this exchange, the GA should not, on average, perform any better than a variety of simpler algorithms that are not population-based. A simple method for testing the usefulness of crossover for a particular problem is presented. This makes it possible to identify situations in which crossover is apparently useful but is in fact producing gains that are only equal to (or less than) those that can be obtained with macromutation and no population.