GA-EDA: Hybrid evolutionary algorithm using genetic and estimation of distribution algorithms

Abstract

Evolutionary techniques are one of the most successful paradigms in the field of optimization. In this paper we present a new approach, named GAEDA, which is a new hybrid algorithm based on genetic and estimation of distribution algorithms. The original objective is to get benefits from both approaches. In order to perform an evaluation of this new approach a selection of synthetic optimizations problems have been proposed together with two real-world cases. Experimental results show the correctness of our new approach.