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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Veröffentlicht in:	Automatic control and computer sciences Jg. 55; H. 7; S. 885 - 902
Hauptverfasser:	Bassin, A. O., Buzdalov, M. V., Shalyto, A. A.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Moscow Pleiades Publishing 01.12.2021 Springer Nature B.V
Schlagworte:	Computer Science Control Structures and Microprogramming Evolutionary algorithms Genetic algorithms Linear functions Performance degradation Performance enhancement parameter adaptation MAX-3SAT linear functions 1 + (λ λ)) GA
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.
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.
Copyright_xml	– notice: 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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Snippet	Self-adjustment of parameters can significantly improve the performance of evolutionary algorithms. A notable example is the (1 + (λ, λ)) genetic algorithm,...
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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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