Efficient Optimisation of Noisy Fitness Functions with Population-based Evolutionary Algorithms
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Publication:2981883
DOI10.1145/2725494.2725508zbMath1361.68195OpenAlexW1989244967MaRDI QIDQ2981883
Per Kristian Lehre, Duc-Cuong Dang
Publication date: 10 May 2017
Published in: Proceedings of the 2015 ACM Conference on Foundations of Genetic Algorithms XIII (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1145/2725494.2725508
Analysis of algorithms and problem complexity (68Q25) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20)
Related Items (9)
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 ⋮ Self-adaptation Can Improve the Noise-tolerance of Evolutionary Algorithms ⋮ More precise runtime analyses of non-elitist evolutionary algorithms in uncertain environments ⋮ The voting algorithm is robust to various noise models ⋮ Exponential upper bounds for the runtime of randomized search heuristics ⋮ Running time analysis of the \((1+1)\)-EA for OneMax and LeadingOnes under bit-wise noise ⋮ Runtime analyses of the population-based univariate estimation of distribution algorithms on LeadingOnes ⋮ Running time analysis of the (1+1)-EA for robust linear optimization
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