maisМоделирование и анализ информационных системModeling and Analysis of Information Systems1818-10152313-5417Yaroslavl State University10.18255/1818-1015-2020-4-488-508mais-1438Research ArticleTheory of ComputingПравило «одной пятой» с возвратами для настройки размера популяции в генетическом алгоритме (1 + (λ,λ))The “One-fifth Rule” with Rollbacks for Self-Adjustment of the Population Size in the (1 + (λ,λ)) Genetic Algorithmhttps://orcid.org/0000-0002-6697-6714БасинАнтон ОлеговичBassinAnton Olegovich

Аспирант

Student

anton.bassin@gmail.comhttps://orcid.org/0000-0002-7120-8824БуздаловМаксим ВикторовичBuzdalovMaxim Viktorovich

Научный сотрудник, кандидат технических наук

Researcher, PhD.

mbuzdalov@gmail.comhttps://orcid.org/0000-0002-2723-2077ШалытоАнатолий АбрамовичShalytoAnatoly Abramovich

Главный научный сотрудник, доктор технических наук

Chief researcher, Doctor

shalyto@mail.ifmo.ruУниверситет ИТМОРоссияITMO UniversityRussian Federation202019122020274488508Copyright © Басин А.О., Буздалов М.В., Шалыто А.А., 20202020Басин А.О., Буздалов М.В., Шалыто А.А.Bassin A.O., Buzdalov M.V., Shalyto A.A.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.mais-journal.ru/jour/article/view/1438

Известно, что настройка параметров может существенно улучшить время работы эволюционных алгоритмов.Ярким примером этого является генетический алгоритм (1 + (λ,λ)), где адаптация размера популяции в процессе работы помогает достичь линейного времени работы на задаче OneMax. Однако если свойства решаемой задачи вступают в конфликт с принципами работы используемого метода настройки параметров, производительность эволюционного алгоритма может существенно ухудшаться. Так, например, происходит при использовании правила «одной пятой» в упомянутом алгоритме при решении задач со слабой корреляцией между приспособленностью и расстоянием до оптимума.В данной работе предлагается модификация правила «одной пятой», существенно снижающая отрицательные эффекты от его использования при их наличии. Показывается, что данная модификация также достигает линейного времени работы на задаче OneMax, при этом ее использование приводит к улучшению производительности на линейных псевдобулевых функциях со случайными весами, а также на некотором классе задач MAX-3SAT.

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 function with random weights, as well as on random satisfiable MAX-3SAT problems.

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The authors declare that there are no conflicts of interest present.