Stagnation detection with randomized local search

DOI10.1007/978-3-030-72904-2_10zbMath1474.68477arXiv2101.12054OpenAlexW3121354401MaRDI QIDQ2233520

Amirhossein Rajabi, Carsten Witt

Publication date: 18 October 2021

Full work available at URL: https://arxiv.org/abs/2101.12054

zbMATH Keywords

local searchruntime analysisrandomized search heuristicsmultimodal functionsself-adjusting algorithms

Mathematics Subject Classification ID

Analysis of algorithms (68W40) Evolutionary algorithms, genetic algorithms (computational aspects) (68W50) Approximation methods and heuristics in mathematical programming (90C59) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20)

Related Items (10)

A rigorous runtime analysis of the \((1 + (\lambda, \lambda))\) GA on jump functions ⋮ Self-adjusting evolutionary algorithms for multimodal optimization ⋮ When move acceptance selection hyper-heuristics outperform metropolis and elitist evolutionary algorithms and when not ⋮ Lower bounds from fitness levels made easy ⋮ 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 ⋮ Do additional target points speed up evolutionary algorithms? ⋮ How majority-vote crossover and estimation-of-distribution algorithms cope with fitness valleys ⋮ Stagnation detection meets fast mutation ⋮ Stagnation detection meets fast mutation

Cites Work

• The impact of random initialization on the runtime of randomized search heuristics
• Randomized local search, evolutionary algorithms, and the minimum spanning tree problem
• The \((1+\lambda)\) evolutionary algorithm with self-adjusting mutation rate
• Optimal static and self-adjusting parameter choices for the \((1+(\lambda ,\lambda ))\) genetic algorithm
• Theory of Evolutionary Computation

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