Linkage learning via probabilistic modeling in the extended compact genetic algorithm (ECGA)

Abstract

For a long time, genetic algorithms (GAs) were not very successful in automatically identifying and exchanging structures consisting of several correlated genes. This problem, referred in the literature as the linkage-learning problem, has been the subject of extensive research for many years. This chapter explores the relationship between the linkage-learning problem and that of learning probability distributions over multi-variate spaces. Herein, it is argued that these problems are equivalent. Using a simple but effective approach to learning distributions, and by implication linkage, this chapter reveals the existence of GA-like algorithms that are potentially orders of magnitude faster and more accurate than the simple GA. © Springer-Verlag Berlin Heidelberg 2006.