Gradual Distributed Real-Coded Genetic Algorithms

Abstract

A major problem in the use of genetic algorithms is premature convergence, a premature stagnation of the search caused by the lack of diversity in the population. One approach for dealing with this problem is the distributed genetic algorithm model. Its basic idea is to keep, in parallel, several subpopulations that are processed by genetic algorithms, with each one being independent of the others. Furthermore, a migration mechanism produces a chromosome exchange between the subpopulations. Making distinctions between the subpopulations 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 premature convergence and reach approximate final solutions. This paper presents the gradual distributed real-coded genetic algorithms, 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 allowed us to differentiate between the subpopulations in this fashion. Using crossover operators presented for real-coded genetic algorithms, we implement three instances of gradual distributed real-coded genetic algorithms. Experimental results show that the proposals consistently outperform sequential real-coded genetic algorithms and homogeneous distributed realcoded genetic algorithms, which are equivalent to them and other mechanisms presented in the literature. These proposals offer two important advantages at the same time: better reliability and accuracy.