Heterogeneous distributed genetic algorithms based on the crossover operator

Abstract

Making distinctions between the subpopulations of a distributed genetic by applying genetic algorithms with different configurations, we obtain the so-called heterogeneous distributed genetic algorithms. These algorithms represent a promising way for introducing a correct exploration/exploitation balance in order to avoid the premature convergence problem and improve the behavior of the search process. In this paper we present a type of heterogeneous distributed real-coded genetic algorithms that apply a different crossover operator to each subpopulation. The importance of this operator on the genetic algorithm's performance made us to differentiate between the subpopulations in this fashion. Experiments carried out with two instances of this approach show that they consistently outperform equivalent sequential genetic algorithms and homogeneous distributed genetic algorithms.