Improving Evolutionary Algorithms with Multi-representation Island Models

Abstract. We present an island model that uses different representations in each island. The model transforms individuals from one representation to another during migrations. We show that such a model helps the evolutionary algorithm to escape from local optima and to solve problems that are difficult for single representation EAs. We illustrate this approach with a two population island model in which one island uses a standard binary encoding and the other island uses a standard reflective Gray code. We compare the performance of this multi-representation island model with single population EAs using only binary or Gray codes. We show that, on a variety of difficult multi-modal test functions, the multi-representation island model does no worse than a standard EA on all of the functions, and produces significant improvements on a subset of them.

• Type: Conference
• Year: 2004
• Where: PPSN
• Authors: Zbigniew Skolicki, Kenneth A. De Jong
• Keywords: Distributed And Parallel Computing, Island Model, Multi-representation Island Model, Population Island Model, PPSN 2004