Self-adaptation Can Improve the Noise-tolerance of Evolutionary Algorithms

DOI10.1145/3594805.3607128OpenAlexW4385437244MaRDI QIDQ6120976

Per Kristian Lehre, Unnamed Author

Publication date: 23 February 2024

Published in: Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1145/3594805.3607128

zbMATH Keywords

evolutionary algorithmsself-adaptationnoisy optimisation

Mathematics Subject Classification ID

Evolutionary algorithms, genetic algorithms (computational aspects) (68W50) Approximation methods and heuristics in mathematical programming (90C59) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20)

Cites Work

• Robustness of populations in stochastic environments
• On the analysis of the \((1+1)\) evolutionary algorithm
• Running time analysis of the \((1+1)\)-EA for OneMax and LeadingOnes under bit-wise noise
• Optimal static and self-adjusting parameter choices for the \((1+(\lambda ,\lambda ))\) genetic algorithm
• Multiplicative up-drift
• Runtime analyses of the population-based univariate estimation of distribution algorithms on LeadingOnes
• Analysis of noisy evolutionary optimization when sampling fails
• Analysing the robustness of evolutionary algorithms to noise: refined runtime bounds and an example where noise is beneficial
• Runtime analysis for self-adaptive mutation rates
• Efficient Optimisation of Noisy Fitness Functions with Population-based Evolutionary Algorithms
• Self-adjusting offspring population sizes outperform fixed parameters on the cliff function

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