Average convergence rate of evolutionary algorithms in continuous optimization

DOI10.1016/j.ins.2020.12.076arXiv1810.11672MaRDI QIDQ6081271

Jun He, Yu Chen

Publication date: 25 October 2023

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1810.11672

zbMATH Keywords

Markov chainconvergence rateevolutionary algorithmapproximation errorcontinuous optimization

Mathematics Subject Classification ID

Nonlinear programming (90C30) Approximation methods and heuristics in mathematical programming (90C59)

Cites Work

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• Log-linear convergence and divergence of the scale-invariant \((1+1)\)-ES in noisy environments
• Markov chains and stochastic stability
• Evolutionary programming using a mixed mutation strategy
• On the convergence rate issues of general Markov search for global minimum
• Convergence results for the (1,\(\lambda\))-SA-ES using the theory of \(\varphi\)-irreducible Markov chains
• On the convergence rates of genetic algorithms
• Quality gain analysis of the weighted recombination evolution strategy on general convex quadratic functions
• Performance analysis of randomised search heuristics operating with a fixed budget
• On geometric convergence rate of Markov search towards the fat target
• Linear Convergence of Comparison-based Step-size Adaptive Randomized Search via Stability of Markov Chains
• Drift conditions for estimating the first hitting times of evolutionary algorithms
• Matrix Iterative Analysis
• Drift analysis and average time complexity of evolutionary algorithms
• Convergence rates for a class of evolutionary algorithms with elitist strategy

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