Run-Time Analysis of Population-Based Evolutionary Algorithm in Noisy Environments

DOI10.1145/2725494.2725498zbMath1361.68204OpenAlexW2085733505MaRDI QIDQ2981884

Jonathan L. Shapiro, Adam Prügel-Bennett, Jonathan E. Rowe

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://www.research.manchester.ac.uk/portal/en/publications/runtime-analysis-of-populationbased-evolutionary-algorithm-in-noisy-environments(b0d1bb36-5d9d-4e0d-b6c4-3ddb0cdeb363).html

zbMATH Keywords

runtime analysisuniform crossovernoisy optimisation

Mathematics Subject Classification ID

Analysis of algorithms and problem complexity (68Q25) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20)

Related Items (7)

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 ⋮ More precise runtime analyses of non-elitist evolutionary algorithms in uncertain environments ⋮ The voting algorithm is robust to various noise models ⋮ Memetic algorithms outperform evolutionary algorithms in multimodal optimisation ⋮ Running time analysis of the \((1+1)\)-EA for OneMax and LeadingOnes under bit-wise noise ⋮ Running time analysis of the (1+1)-EA for robust linear optimization

Cites Work

• Approximating fixation probabilities in the generalized Moran process
• On the fixation probability of superstars
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