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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 efficacy. 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 operator 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 the premature convergence problem and improve the behavior of the search process. In this paper, we present the gradual distributed 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 made us to differentiate between the subpopulations in this fashion. Using crossover operators presented for real-coded genetic algorithms, we implement four instances of gradual distributed genetic algorithms. Experimental results show that the proposals consistently outperform equivalent sequential genetic algorithms and homogeneous distributed genetic algorithms, offering two main advantages at the same time, better reliability and accuracy.
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