Stagnation detection meets fast mutation

DOI10.1007/978-3-031-04148-8_13zbMath1499.68430arXiv2201.12158OpenAlexW4225747923MaRDI QIDQ5918778

Benjamin Doerr, Amirhossein Rajabi

Publication date: 11 August 2022

Published in: Evolutionary Computation in Combinatorial Optimization (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2201.12158

zbMATH Keywords

theorymutation operatorparameter controljump functions

Mathematics Subject Classification ID

Evolutionary algorithms, genetic algorithms (computational aspects) (68W50) Approximation methods and heuristics in mathematical programming (90C59)

Related Items (3)

Lazy parameter tuning and control: choosing all parameters randomly from a power-law distribution ⋮ An extended jump functions benchmark for the analysis of randomized search heuristics ⋮ Stagnation detection meets fast mutation

Cites Work

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• When a genetic algorithm outperforms hill-climbing
• On the analysis of the \((1+1)\) evolutionary algorithm
• Self-adjusting evolutionary algorithms for multimodal optimization
• Fast mutation in crossover-based algorithms
• Stagnation detection with randomized local search
• On crossing fitness valleys with majority-vote crossover and estimation-of-distribution algorithms
• Automatic adaptation of hypermutation rates for multimodal optimisation
• Theory of Evolutionary Computation

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