Emergence of Structural Bias in Differential Evolution
| Autor: | Stein, B. van, Caraffini, F., Kononova, A.V., Chicano, F. |
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| Přispěvatelé: | Chicano, F. |
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Mathematical optimization algorithmic behaviour Computer science Heuristic (computer science) differential evolution Crossover Computer Science - Neural and Evolutionary Computing 0102 computer and information sciences 02 engineering and technology parameter setting 01 natural sciences Domain (software engineering) 010201 computation theory & mathematics structural bias Differential evolution Mutation (genetic algorithm) 0202 electrical engineering electronic engineering information engineering Test functions for optimization 020201 artificial intelligence & image processing Almost surely Neural and Evolutionary Computing (cs.NE) Heuristics constraints handling |
| Zdroj: | GECCO Companion Proceedings of the Genetic and Evolutionary Computation Conference Companion, 1234-1242. ACM STARTPAGE=1234;ENDPAGE=1242;TITLE=Proceedings of the Genetic and Evolutionary Computation Conference Companion |
| DOI: | 10.48550/arxiv.2105.04693 |
| Popis: | 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. 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. |
| Databáze: | OpenAIRE |
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