A large population size can be unhelpful in evolutionary algorithms

DOI10.1016/J.TCS.2011.02.016zbMath1242.68286arXiv1208.2345OpenAlexW2091342063MaRDI QIDQ428903

Tianshi Chen, Ke Tang, Xin Yao, Guo-Liang Chen

Publication date: 25 June 2012

Published in: Theoretical Computer Science (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1208.2345

zbMATH Keywords

combinatorial optimizationevolutionary computationevolutionary algorithmsruntime analysiscomputational time complexity

Mathematics Subject Classification ID

Analysis of algorithms (68W40) Combinatorial optimization (90C27) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20)

Related Items (8)

Evolutionary optimization: pitfalls and booby traps ⋮ On the equality constraints tolerance of constrained optimization problems ⋮ The use of tail inequalities on the probable computational time of randomized search heuristics ⋮ An image-guided computational approach to inversely determine \textit{in vivo} material properties and model flow-structure interactions of fish fins ⋮ Performance analysis of the \((1+1)\) evolutionary algorithm for the multiprocessor scheduling problem ⋮ Global versus local search: the impact of population sizes on evolutionary algorithm performance ⋮ On the genetic algorithm with adaptive mutation rate and selected statistical applications ⋮ An evolutionary approach to optimizing teleportation cost in distributed quantum computation

Cites Work

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• Choosing selection pressure for wide-gap problems
• On the analysis of the \((1+1)\) evolutionary algorithm
• A study of drift analysis for estimating computation time of evolutionary algorithms
• On the impact of the mutation-selection balance on the runtime of evolutionary algorithms
• Drift analysis and average time complexity of evolutionary algorithms

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