Larger Offspring Populations Help the (1 + (λ, λ)) Genetic Algorithm to Overcome the Noise
Date
2023
Authors
Ivanova, A.
Antipov, D.
Doerr, B.
Editors
Paquete, L.
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Conference paper
Citation
Proceedings of the Genetic and Evolutionary Computation Conference (GECCO, 2023), 2023 / Paquete, L. (ed./s), pp.919-928
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Alexandra Ivanova, Denis Antipov, Benjamin Doerr
Conference Name
Genetic and Evolutionary Computation Conference (GECCO) (15 Jul 2023 - 19 Jul 2023 : Lisbon, Portugal)
Abstract
Evolutionary algorithms are known to be robust to noise in the
evaluation of the fitness. In particular, larger offspring population
sizes often lead to strong robustness. We analyze to what extent
the (1 + (𝜆, 𝜆)) genetic algorithm is robust to noise. This algorithm
also works with larger offspring population sizes, but an intermediate selection step and a non-standard use of crossover as repair
mechanism could render this algorithm less robust than, e.g., the
simple (1 + 𝜆) evolutionary algorithm. Our experimental analysis
on several classic benchmark problems shows that this difficulty
does not arise. Surprisingly, in many situations this algorithm is
even more robust to noise than the (1 + 𝜆) EA.
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