Gradual Distributed Real - Coded Genetic Algorithms 1

Genetic algorithm behavior is determined by the exploration/exploitation balance kept throughout the run. When this balance is disproportionate, the premature convergence problem will probably appear, causing a drop in the genetic algorithm's eecacy. One approach presented 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 from the others. Furthermore, a migration mechanism produces a chromosome exchange between the sub-populations. Making distinctions between the subpopulations by applying genetic algorithms with diierent conngurations, 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 reach approximate nal solutions. In this paper, we present the gradual distributed real-coded genetic algorithms, a type of heterogeneous distributed real-coded genetic algorithms that apply a diierent crossover operator to each subpopulation. The importance of this operator on the genetic algorithm's performance made us to diierentiate 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 real-coded genetic algorithms, which are equivalent to them, and other mechanisms presented in the literature for dealing with the premature convergence, ooering two main advantages at the same time, better reliability and accuracy.