Résumé
One hope of using non-elitism in evolutionary computation is that it aids leaving local optima. We perform a rigorous runtime analysis of a basic non-elitist evolutionary algorithm (EA), the (μ, λ) EA, on the most basic benchmark function with a local optimum, the jump function. We prove that for all reasonable values of the parameters and the problem, the expected runtime of the (μ, λ) EA is, apart from lower order terms, at least as large as the expected runtime of its elitist counterpart, the (μ + λ) EA (for which we conduct the first runtime analysis to allow this comparison). Consequently, the ability of the (μ, λ) EA to leave local optima to inferior solutions does not lead to a runtime advantage. We complement this lower bound with an upper bound that, for broad ranges of the parameters, is identical to our lower bound apart from lower order terms. This is the first runtime result for a non-elitist algorithm on a multi-modal problem that is tight apart from lower order terms.
| langue originale | Anglais |
|---|---|
| titre | GECCO 2020 - Proceedings of the 2020 Genetic and Evolutionary Computation Conference |
| Editeur | Association for Computing Machinery |
| Pages | 1304-1313 |
| Nombre de pages | 10 |
| ISBN (Electronique) | 9781450371285 |
| Les DOIs | |
| état | Publié - 25 juin 2020 |
| Evénement | 2020 Genetic and Evolutionary Computation Conference, GECCO 2020 - Cancun, Mexique Durée: 8 juil. 2020 → 12 juil. 2020 |
Série de publications
| Nom | GECCO 2020 - Proceedings of the 2020 Genetic and Evolutionary Computation Conference |
|---|
Une conférence
| Une conférence | 2020 Genetic and Evolutionary Computation Conference, GECCO 2020 |
|---|---|
| Pays/Territoire | Mexique |
| La ville | Cancun |
| période | 8/07/20 → 12/07/20 |
SDG des Nations Unies
Ce résultat contribue à ou aux Objectifs de développement durable suivants
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SDG 3 Bonne santé et bien-être
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