Crossover, Macromutation, and Population-Based Search

Abstract

A major reason for the maintenance of a population in a Genetic Algorithm (GA) is the hope of increased performance via direct communication of information between individuals. This communication is achieved through the use of a crossover operator. If crossover is not a useful method for this exchange, the GA may 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 instance is presented. This allows the identification of situations in which crossover is apparently useful but is actually only producing gains that could be obtained, or exceeded, with macromutation and no population. 1 INTRODUCTION The features of a GA that distinguish it most from other search methods are its use of a population and a crossover operator. In a GA, crossover is employed to effect direct communication between individuals in a population. The usefulness of crossover, and theref...