Hybrid Models of Solving Optimization Tasks on the Basis of Integrating Evolutionary Design and Multiagent Technologies

Gladkov, Leonid A.; Gladkova, Nadezhda V.; Gromov, S. A.

doi:10.1007/978-3-030-19810-7_38

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…Эволюционные алгоритмы применяются для решения множества практических задач, таких как составление расписаний в промышленности [15,16], оптимизация разводки печатных плат [17][18][19][20], построение систем управления [21][22][23][24][25][26][27], сегментирование изображений [28], а также приближенного решения других задач комбинаторной [29][30][31][32] и вещественной оптимизации [33][34][35].…”

Section: Introductionunclassified

The “One-Fifth Rule” with Rollbacks for Self-Adjustment of the Population Size in the (1 + (λ, λ)) Genetic Algorithm

Bassin

Buzdalov

Shalyto

2021

Aut. Control Comp. Sci.

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Self-adjustment of parameters can signi cantly improve the performance of evolutionary algorithms. A notable example is the (1 + ( , )) 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-h rule" on problems with weak tness-distance correlation. We propose a modi cation of the "one-h rule" in order to have less negative impact on the performance in the cases where the original rule is destructive. Our modi cation, while still yielding a provable linear runtime on OneMax, shows be er results on linear function with random weights, as well as on random satis able MAX-3SAT problems.

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Section: Introductionunclassified

The “One-Fifth Rule” with Rollbacks for Self-Adjustment of the Population Size in the (1 + (λ, λ)) Genetic Algorithm

Bassin

Buzdalov

Shalyto

2021

Aut. Control Comp. Sci.

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“…if ( ) > ( ) then ⊳ Selection and adaptation 16: Правило «одной пятой», используемое для адаптивной настройки параметра , устроено следующим образом. Если лучшая приспособленность на текущей итерации увеличилась, то значение уменьшается в раз.…”

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The “One-fifth Rule” with Rollbacks for Self-Adjustment of the Population Size in the (1 + (λ,λ)) Genetic Algorithm

Басин

Буздалов

Шалыто

2020

Model. anal. inf. sist.

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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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Construction of Evolving Multi-Agent Systems Based on the Principles of Evolutionary Design

Gladkov

Gladkova

Gromov

2021

Lecture Notes in Electrical Engineering

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Hybrid Models of Solving Optimization Tasks on the Basis of Integrating Evolutionary Design and Multiagent Technologies

Cited by 3 publications

References 5 publications

The “One-Fifth Rule” with Rollbacks for Self-Adjustment of the Population Size in the (1 + (λ, λ)) Genetic Algorithm

The “One-Fifth Rule” with Rollbacks for Self-Adjustment of the Population Size in the (1 + (λ, λ)) Genetic Algorithm

The “One-fifth Rule” with Rollbacks for Self-Adjustment of the Population Size in the (1 + (λ,λ)) Genetic Algorithm

Construction of Evolving Multi-Agent Systems Based on the Principles of Evolutionary Design

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