Performance analysis of evolution strategies with multi-recombination in high-dimensional \(\mathbb R^N\)-search spaces disturbed by noise.

DOI10.1016/S0304-3975(01)00384-XzbMath1060.90083MaRDI QIDQ1853550

Hans-Georg Beyer, Dirk V. Arnold

Publication date: 21 January 2003

Published in: Theoretical Computer Science (Search for Journal in Brave)

zbMATH Keywords

OptimizationDarwinian evolutionNoiseEvolution strategies

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Approximation methods and heuristics in mathematical programming (90C59)

Related Items (4)

Convergence Properties of the (μ/μI, λ)-ES on the Rastrigin Function ⋮ Weighted multirecombination evolution strategies ⋮ Comparing evolutionary algorithms to the (\(1+1\))-EA ⋮ How to analyse evolutionary algorithms.

Cites Work

• Evolutionary algorithms in noisy environments: theoretical issues and guidelines for practise.
• Theory of evolutionary algorithms: A bird's eye view
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