Adaptive estimation of distribution algorithms

Abstract

Estimation of distribution algorithms (EDAs) are evolutionary methods that use probabilistic models instead of genetic operators to lead the search. Most of current proposals on EDAs do not incorporate adaptive techniques. Usually, the class of probabilistic model employed as well as the learning and sampling methods are static. In this paper, we present a general framework for introducing adaptation in EDAs. This framework allows the possibility of changing the class of probabilistic models during the evolution. We present a number of measures, and techniques that can be used to evaluate the effect of the EDA components in order to design adaptive EDAs. As a case of study we present an adaptive EDA that combines different classes of probabilistic models and sampling methods. The algorithm is evaluated in the solution of the satisfiability problem. © 2008 Springer-Verlag Berlin Heidelberg.