Estimation of the Evolution Speed for the Quasispecies Model: Arbitrary Alphabet Case

Red’ko, Vladimir G.; Tsoy, Yuri

doi:10.1007/11785231_49

Cited by 2 publications

(2 citation statements)

References 4 publications

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“…С обширным перечнем известных результатов и их обсуждением можно ознакомиться в обзорах [54,55]. Среди российских ученых стоит отметить В. Редько [56,57], Е. Семенкина [58,59], С. Родзина [60,61], А. Еремеева [62][63][64][65].…”

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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show abstract

Section: Introductionunclassified

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.

show abstract

Estimation of the Evolution Speed for the Quasispecies Model: Arbitrary Alphabet Case

Cited by 2 publications

References 4 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

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