The “One-Fifth Rule” with Rollbacks for Self-Adjustment of the Population Size in the (1 + (λ, λ)) Genetic Algorithm
Self-adjustment of parameters can significantly improve the performance of evolutionary algorithms. A notable example is the (1 + (λ, λ)) genetic algorithm, where adaptation of the population size helps to achieve the linear running time on the OneMax problem. However, on problems which interfere wi...
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| Vydáno v: | Automatic control and computer sciences Ročník 55; číslo 7; s. 885 - 902 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Moscow
Pleiades Publishing
01.12.2021
Springer Nature B.V |
| Témata: | |
| ISSN: | 0146-4116, 1558-108X |
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| Abstract | Self-adjustment of parameters can significantly improve the performance of evolutionary algorithms. A notable example is the (1 + (λ, λ)) genetic algorithm, where adaptation of the population size helps to achieve the linear running time on the OneMax problem. However, on problems which interfere with the assumptions behind the self-adjustment procedure, its usage can lead to the performance degradation. In particular, this is the case with the “one-fifth rule” on problems with weak fitness-distance correlation. We propose a modification of the “one-fifth rule” in order to have less negative impact on the performance in the cases where the original rule is destructive. Our modification, while still yielding a provable linear runtime on OneMax, shows better results on linear functions with random weights, as well as on random satisfiable MAX-3SAT problems. |
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| AbstractList | Self-adjustment of parameters can significantly improve the performance of evolutionary algorithms. A notable example is the (1 + (λ, λ)) genetic algorithm, where adaptation of the population size helps to achieve the linear running time on the OneMax problem. However, on problems which interfere with the assumptions behind the self-adjustment procedure, its usage can lead to the performance degradation. In particular, this is the case with the “one-fifth rule” on problems with weak fitness-distance correlation. We propose a modification of the “one-fifth rule” in order to have less negative impact on the performance in the cases where the original rule is destructive. Our modification, while still yielding a provable linear runtime on OneMax, shows better results on linear functions with random weights, as well as on random satisfiable MAX-3SAT problems. |
| Author | Bassin, A. O. Buzdalov, M. V. Shalyto, A. A. |
| Author_xml | – sequence: 1 givenname: A. O. orcidid: 0000-0002-6697-6714 surname: Bassin fullname: Bassin, A. O. email: anton.bassin@gmail.com organization: ITMO University – sequence: 2 givenname: M. V. orcidid: 0000-0002-7120-8824 surname: Buzdalov fullname: Buzdalov, M. V. email: mbuzdalov@gmail.com organization: ITMO University – sequence: 3 givenname: A. A. orcidid: 0000-0002-2723-2077 surname: Shalyto fullname: Shalyto, A. A. organization: ITMO University |
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| Copyright | Allerton Press, Inc. 2021. ISSN 0146-4116, Automatic Control and Computer Sciences, 2021, Vol. 55, No. 7, pp. 885–902. © Allerton Press, Inc., 2021. Russian Text © The Author(s), 2020, published in Modelirovanie i Analiz Informatsionnykh Sistem, 2020, No. 4, pp. 488–508. |
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| SubjectTerms | Computer Science Control Structures and Microprogramming Evolutionary algorithms Genetic algorithms Linear functions Performance degradation Performance enhancement |
| Title | The “One-Fifth Rule” with Rollbacks for Self-Adjustment of the Population Size in the (1 + (λ, λ)) Genetic Algorithm |
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