Linearly Convergent Evolution Strategies via Augmented Lagrangian Constraint Handling

DOI10.1145/3040718.3040732zbMath1365.68383OpenAlexW2586561270MaRDI QIDQ5738867

Asma Atamna, Nikolaus Hansen, Anne Auger

Publication date: 13 June 2017

Published in: Proceedings of the 14th ACM/SIGEVO Conference on Foundations of Genetic Algorithms (Search for Journal in Brave)

Full work available at URL: https://hal.inria.fr/hal-01455379v2/file/FOGA-2017.pdf

zbMATH Keywords

constrained optimizationMarkov chainevolution strategiesaugmented Lagrangianrandomized optimization algorithms

Mathematics Subject Classification ID

Approximation methods and heuristics in mathematical programming (90C59) Applications of Markov chains and discrete-time Markov processes on general state spaces (social mobility, learning theory, industrial processes, etc.) (60J20) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20) Randomized algorithms (68W20)

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On invariance and linear convergence of evolution strategies with augmented Lagrangian constraint handling ⋮ Analysis of the \((1,\lambda)\)-\(\sigma\)-self-adaptation evolution strategy with repair by projection applied to a conically constrained problem

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