Structured population-based incremental learning

Abstract

 We investigate a recently developed abstraction of genetic algorithms (GAs) in which a population of GAs in any generation is represented by a single vector whose elements are the probabilities of the corresponding bit positions being equivalent to 1. The process of evolution is represented by learning the elements of the probability vector; the method is clearly linked to the artificial neural network (ANN) method of competitive learning. We use techniques from ANNs to extend the applicability of the method to non-static problems, to multi-objective criteria, to multi-modal problems and to creating an order on a set of sub-populations.