Static and self-adjusting mutation strengths for multi-valued decision variables

DOI10.1007/s00453-017-0341-1zbMath1390.68593OpenAlexW2733925684MaRDI QIDQ1750364

Benjamin Doerr, Carola Doerr, Timo Kötzing

Publication date: 18 May 2018

Published in: Algorithmica (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00453-017-0341-1

zbMATH Keywords

genetic algorithmsparameter choiceruntime analysistheory of randomized search heuristicsparameter control

Mathematics Subject Classification ID

Analysis of algorithms (68W40) Approximation methods and heuristics in mathematical programming (90C59) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20) Randomized algorithms (68W20)

Related Items (9)

Self-adjusting evolutionary algorithms for multimodal optimization ⋮ Runtime analysis for self-adaptive mutation rates ⋮ Self-adjusting population sizes for the (1,\( \lambda )\)-EA on monotone functions ⋮ First Steps Towards a Runtime Analysis of Neuroevolution ⋮ Lower bounds from fitness levels made easy ⋮ Lazy parameter tuning and control: choosing all parameters randomly from a power-law distribution ⋮ Self-adjusting population sizes for non-elitist evolutionary algorithms: why success rates matter ⋮ An extended jump functions benchmark for the analysis of randomized search heuristics ⋮ Curing ill-Conditionality via Representation-Agnostic Distance-Driven Perturbations

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