Emergence of structural bias in differential evolution

Genetic and Evolutionary Computation Conference Companion (GECCO Companion), pp. 1234–1242

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 a 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.

Authors 3

1. Bas van Stein Aachen

   Leiden University

   Affiliation as printed

   Leiden University, The Netherlands

2. Fabio Caraffini

   De Montfort University

   Affiliation as printed

   De Montfort University, Leicester, UK

3. Anna V. Kononova Aachen

   Leiden University

   Affiliation as printed

   Leiden University, The Netherlands