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.
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Skolicki, Z., De Jong, K. (2004). Improving Evolutionary Algorithms with Multi-representation Island Models. In: Yao, X., et al. Parallel Problem Solving from Nature - PPSN VIII. PPSN 2004. Lecture Notes in Computer Science, vol 3242. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30217-9_43
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DOI: https://doi.org/10.1007/978-3-540-30217-9_43
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