Analysis of noisy evolutionary optimization when sampling fails

DOI10.1007/s00453-019-00666-6OpenAlexW3002251818WikidataQ126329573 ScholiaQ126329573MaRDI QIDQ2661993

Chao Qian, Xin Yao, Ke Tang, Chao Bian, Yang Yu

Publication date: 8 April 2021

Published in: Algorithmica (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1810.05045

zbMATH Keywords

samplingpopulationevolutionary algorithmsnoisy optimizationadaptive samplingrunning time analysis

Mathematics Subject Classification ID

Algorithms in computer science (68Wxx) Graph theory (05Cxx)

Related Items (4)

Self-adaptation Can Improve the Noise-tolerance of Evolutionary Algorithms ⋮ The cost of randomness in evolutionary algorithms: crossover can save random bits ⋮ More precise runtime analyses of non-elitist evolutionary algorithms in uncertain environments ⋮ Modeling the dynamics of a changing range genetic algorithm in noisy environments

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