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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Bibliographic Details
Published in:Automatic control and computer sciences Vol. 55; no. 7; pp. 885 - 902
Main Authors: Bassin, A. O., Buzdalov, M. V., Shalyto, A. A.
Format: Journal Article
Language:English
Published: Moscow Pleiades Publishing 01.12.2021
Springer Nature B.V
Subjects:
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
Online Access:Get full text
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Summary: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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ISSN:0146-4116
1558-108X
DOI:10.3103/S0146411621070208