Enhancing diversity for a genetic algorithm learning environment for classification tasks

Abstract

The paper describes an inductive learning environment called DELVAUX for classification tasks that learns PROSPECTOR-style, Bayesian rules from sets of examples. A genetic algorithm approach is used for learning Bayesian rule-sets, in which a population consists of sets of rule-sets that generate offspring through the exchange of rules, permitting fitter rule-sets to produce offspring with a higher probability. To deal with the premature convergence problem, fuzzy similarity measures for Bayesian rule-sets are introduced and the genetic algorithm approach is modified, so that similar rule-sets produce offspring with a lower probability, relying on a sharing function approach. Empirical results are presented that evaluate the benefits of the sharing function approach in our learning environment.<>