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

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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.

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Funding

This research was supported by the Russian Scientific Foundation, agreement no. 17-71-20178.

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  1. ITMO University, 197101, St. Petersburg, Russia

    A. O. Bassin, M. V. Buzdalov & A. A. Shalyto

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  1. A. O. Bassin
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Correspondence to A. O. Bassin or M. V. Buzdalov.

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Bassin, A.O., Buzdalov, M.V. & Shalyto, A.A. The “One-Fifth Rule” with Rollbacks for Self-Adjustment of the Population Size in the (1 + (λ, λ)) Genetic Algorithm. Aut. Control Comp. Sci. 55, 885–902 (2021). https://doi.org/10.3103/S0146411621070208

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