Emergence of Structural Bias in Differential Evolution

Date

2021-07

Advisors

Journal Title

Journal ISSN

ISSN

Volume Title

Publisher

ACM

Type

Conference

Peer reviewed

Yes

Abstract

Heuristic optimisation algorithms are in high demand due to the overwhelming amount of complex optimisation problems that need to be solved. The complexity of these problems is well beyond the boundaries of applicability of exact optimisation algorithms and therefore require modern heuristics to find feasible solutions quickly. These heuristics and their effects are almost always evaluated and explained by particular problem instances. In previous works, it has been shown that many such algorithms show structural bias, by either being attracted to a certain region of the search space or by consistently avoiding regions of the search space, on. special test function designed to ensure uniform 'exploration' of the domain. In this paper, we analyse the emergence of such structural bias for Differential Evolution (DE) configurations and, specifically, the effect of different mutation, crossover and correction strategies. We also analyse the emergence of the structural bias during the run-time of each algorithm. We conclude with recommendations of which configurations should be avoided in order to run DE unbiased.

Description

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.

Keywords

structural bias, algorithmic behaviour, differential evolution, parameter setting, constraints handling

Citation

van Stein, B., Caraffini, F. and Kononova, A.V. (2021) Emergence of Structural Bias in Differential Evolution. In: Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion, Lille, France (GECCO ’21 Companion), July 2021, New York: Association for Computing Machinery.

Rights

Research Institute

Institute of Artificial Intelligence (IAI)