Using assortative mating in genetic algorithms for vector quantization problems
Carlos M. C. Fernandes, Rui Santos‐Tavares, Cristian Munteanu, Agostinho C. Rosa · 2001
In nature, some species mate according to their phenotype similarity. The Assortative Mating Genetic Algorithm (AMGA) mimics some mechanisms of reproduction in natural environments. The main difference between AMGA and the Standard GA (SGA) is the selection of the parents in the crossover operators. We develop a similarity measure for the Vector Quantization problem and we show that the application of AMGA to some instances of this problem reduces the number of times that the algorithm becomes trapped in local optima. We also present results that show that AMGA keeps a higher level of genetic diversity than the SGA. 1. INTRODUCTION Genetic Algorithms (GAs) [12] [14] are adaptive systems inspired by natural evolution. The Standard GA (SGA) randomly creates an initial population of solutions, also called chromosomes. Crossover operator recombines these solutions over a certain number of generations until a stop criterion is reached. The chromosomes to recombine -- parents - are selecte...