Preprint Article Version 1 This version is not peer-reviewed

Can Compact Optimisation Algorithms Be Structurally Biased?

Version 1 : Received: 22 April 2020 / Approved: 23 April 2020 / Online: 23 April 2020 (04:49:23 CEST)

How to cite: Kononova, A.V.; Caraffini, F.; Wang, H.; Bäck, T. Can Compact Optimisation Algorithms Be Structurally Biased?. Preprints 2020, 2020040403 (doi: 10.20944/preprints202004.0403.v1). Kononova, A.V.; Caraffini, F.; Wang, H.; Bäck, T. Can Compact Optimisation Algorithms Be Structurally Biased?. Preprints 2020, 2020040403 (doi: 10.20944/preprints202004.0403.v1).

Abstract

In the field of stochastic optimisation, the so-called structural bias constitutes an undesired behaviour of an algorithm that is unable to explore the search space to a uniform extent. In this paper, we investigate whether algorithms from a subclass of estimation of distribution algorithms, the compact algorithms, exhibit structural bias. Our approach, justified in our earlier publications, is based on conducting experiments on a test function whose values are uniformly distributed in its domain. For the experiment, 81 combinations of compact algorithms and strategies of dealing with infeasible solutions have been selected as test cases. We have applied two approaches for determining the presence and severity of structural bias, namely a visual and a statistical (Anderson-Darling) tests. Our results suggest that compact algorithms are more immune to structural bias than their counterparts maintaining explicit populations. Both tests indicate that strong structural bias is found only in one of the algorithms (cBFO) regardless of the choice of strategy of dealing with infeasible solutions and cPSO mirror. For other test cases, statistical and visual tests disagree on some cases classified as having mild or strong structural bias: the former one tends to make harsher decisions, thus needing further investigation.

Supplementary and Associated Material

Subject Areas

structural bias; compact algorithm; continuous optimisation; estimation of distribution algorithm; infeasible solution

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