Academic Journal
The Importance of Being Constrained: Dealing with Infeasible Solutions in Differential Evolution and Beyond.
| Τίτλος: | The Importance of Being Constrained: Dealing with Infeasible Solutions in Differential Evolution and Beyond. |
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| Συγγραφείς: | Kononova AV; LIACS, Leiden University, The Netherlands a.kononova@liacs.leidenuniv.nl., Vermetten D; LIACS, Leiden University, The Netherlands d.l.vermetten@liacs.leidenuniv.nl., Caraffini F; Department of Computer Science, Swansea University, United Kingdom fabio.caraffini@swansea.ac.uk., Mitran MA; Department of Computer Science, West University of Timişoara, Romania madalina.mitran96@e-uvt.ro., Zaharie D; Department of Computer Science, West University of Timişoara, Romania daniela.zaharie@e-uvt.ro. |
| Πηγή: | Evolutionary computation [Evol Comput] 2024 Mar 01; Vol. 32 (1), pp. 3-48. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: MIT Press Country of Publication: United States NLM ID: 9513581 Publication Model: Print Cited Medium: Internet ISSN: 1530-9304 (Electronic) Linking ISSN: 10636560 NLM ISO Abbreviation: Evol Comput Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Cambridge, Mass. : MIT Press, c1993- |
| Ιατρικοί όροι (MeSH): | Algorithms* , Gastropoda*, Animals ; Reproducibility of Results ; Heuristics |
| Περίληψη: | We argue that results produced by a heuristic optimisation algorithm cannot be considered reproducible unless the algorithm fully specifies what should be done with solutions generated outside the domain, even in the case of simple bound constraints. Currently, in the field of heuristic optimisation, such specification is rarely mentioned or investigated due to the assumed triviality or insignificance of this question. Here, we demonstrate that, at least in algorithms based on Differential Evolution, this choice induces notably different behaviours in terms of performance, disruptiveness, and population diversity. This is shown theoretically (where possible) for standard Differential Evolution in the absence of selection pressure and experimentally for the standard and state-of-the-art Differential Evolution variants, on a special test function and the BBOB benchmarking suite, respectively. Moreover, we demonstrate that the importance of this choice quickly grows with problem dimensionality. Differential Evolution is not at all special in this regard-there is no reason to presume that other heuristic optimisers are not equally affected by the aforementioned algorithmic choice. Thus, we urge the heuristic optimisation community to formalise and adopt the idea of a new algorithmic component in heuristic optimisers, which we refer to as the strategy of dealing with infeasible solutions. This component needs to be consistently: (a) specified in algorithmic descriptions to guarantee reproducibility of results, (b) studied to better understand its impact on an algorithm's performance in a wider sense (i.e., convergence time, robustness, etc.), and (c) included in the (automatic) design of algorithms. All of these should be done even for problems with bound constraints. (© 2023 Massachusetts Institute of Technology.) |
| Contributed Indexing: | Keywords: Bound constraint; algorithmic behaviour |
| Entry Date(s): | Date Created: 20230515 Date Completed: 20240304 Latest Revision: 20250106 |
| Update Code: | 20260130 |
| DOI: | 10.1162/evco_a_00333 |
| PMID: | 37186673 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1530-9304 |
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| DOI: | 10.1162/evco_a_00333 |