A Parallel Island Model for Estimation of Distribution Algorithms

Abstract

In this work we address the parallelization of the kind of Evolutionary Algorithms (EAs) known as Estimation of Distribution Algorithms (EDAs). After an initial discussion on the types of potentially parallel schemes for EDAs, we proceed to design a distributed island version (dEDA), aimed at improving the numerical efficiency of the sequential algorithm in terms of the number of evaluations. After evaluating such a dEDA on several well-known discrete and continuous test problems, we conclude that our model clearly outperforms existing centralized approaches from a numerical point of view, as well as speeding up the search considerably, thanks to its suitability for physical parallelism.