The benefits and limitations of voting mechanisms in evolutionary optimisation

DOI10.1145/3299904.3340305zbMath1433.68651OpenAlexW2967587352MaRDI QIDQ5215478

Jonathan E. Rowe Aishwaryaprajna, Jonathan E. Rowe

Publication date: 11 February 2020

Published in: Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms (Search for Journal in Brave)

Full work available at URL: https://research.birmingham.ac.uk/portal/en/publications/the-benefits-and-limitations-of-voting-mechanisms-in-evolutionary-optimisation(a736e982-7118-4cb1-992a-a1a31e59c35c).html

zbMATH Keywords

noisevotingcrossover

Mathematics Subject Classification ID

Analysis of algorithms (68W40) Evolutionary algorithms, genetic algorithms (computational aspects) (68W50) Approximation methods and heuristics in mathematical programming (90C59)

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A rigorous runtime analysis of the \((1 + (\lambda, \lambda))\) GA on jump functions ⋮ Does comma selection help to cope with local optima? ⋮ Self-adjusting evolutionary algorithms for multimodal optimization ⋮ Choosing the right algorithm with hints from complexity theory ⋮ Lower bounds from fitness levels made easy ⋮ More precise runtime analyses of non-elitist evolutionary algorithms in uncertain environments ⋮ 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 ⋮ Runtime analysis for permutation-based evolutionary algorithms ⋮ How majority-vote crossover and estimation-of-distribution algorithms cope with fitness valleys ⋮ The voting algorithm is robust to various noise models ⋮ Symmetry breaking for voting mechanisms ⋮ The runtime of the compact genetic algorithm on jump functions

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