Genotypic and Phenotypic Assortative Mating in Genetic Algorithm

Three new methods of selection of mating pairs for Genetic Algorithms (GAs) are introduced where the partners are chosen based on either their genotypic similarity (called genotypic assortative mating) or their phenotypic similarity (called phenotypic assortative mating). These methods not only help in exploiting the current search space properly before exploring the new one but also enable one to mimic inbreeding of natural genetics. A comparative study in terms of disruption of schema due to crossover is made between these methods and conventional genetic algorithm (CGA). The superiority of this new methodology over the CGA and the incest prevention algorithm is established on some problems of optimizing complex functions and selecting optimal neural network parameters. © 1998 Elsevier Science Inc. All rights reserved.