Preprint Article Version 1 This version is not peer-reviewed

Infeasibility and Structural Bias in Differential Evolution

Version 1 : Received: 23 April 2019 / Approved: 24 April 2019 / Online: 24 April 2019 (12:16:42 CEST)

A peer-reviewed article of this Preprint also exists.

Caraffini, F.; Kononova, A.V.; Corne, D. Infeasibility and structural bias in Differential Evolution. Information Sciences 2019, 496, 161-179. Caraffini, F.; Kononova, A.V.; Corne, D. Infeasibility and structural bias in Differential Evolution. Information Sciences 2019, 496, 161-179.

Journal reference: Information Sciences 2019
DOI: 10.1016/j.ins.2019.05.019

Abstract

This paper investigates a range of popular differential evolution (DE) configurations to identify components responsible for emergence of structural bias – a recently identified tendency of algorithms to prefer some regions of search space over others, for reasons unrelated to objective function values. Previous work has explored this tendency for genetic algorithms (GA) and particle swarm optimisation (PSO), finding a relationship between population size and extent of structural bias, hence highlighting potential weaknesses of those algorithms. In current article, we focus on DE, extend the investigation to include consideration of an algorithmic component that is often overlooked – constraint handling mechanism. Towards this end, a wide range of DE configurations was tested here. Results suggest that DE is generally robust to structural bias. Unlike the case with GA and PSO, population size seems to have no influence on DE structural bias. Only one of variants studied – DE/current-to-best/1/bin – shows clear signs of bias, however, we show that this effect is mitigated by a judicious choice of constraint handling technique. These findings contribute towards explaining widespread success of DE variants in algorithm comparison studies; its robustness to structural bias represents the absence of a factor that may confound other algorithms.

Supplementary and Associated Material

Subject Areas

structural bias, algorithmic design, differential evolution, population-based algorithms, optimisation

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