A Markov chain that models genetic algorithms in noisy environments

DOI10.1016/j.na.2009.01.056zbMath1238.60083OpenAlexW2092651480MaRDI QIDQ419865

Takéhiko Nakama

Publication date: 20 May 2012

Published in: Nonlinear Analysis. Theory, Methods \& Applications. Series A: Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.na.2009.01.056

zbMATH Keywords

convergencegenetic algorithmsevolutionary computationadditive noiseMarkov chain analysisnoisy environmentsperturbed fitness functions

Mathematics Subject Classification ID

Applications of Markov chains and discrete-time Markov processes on general state spaces (social mobility, learning theory, industrial processes, etc.) (60J20) Genetics and epigenetics (92D10)

Related Items (1)

Markov chain analysis of self-organizing mobile nodes self-organizing mobile nodes

Cites Work

• A simulation-based multi-objective genetic algorithm (SMOGA) procedure for BOT network design problem
• Characterizing crossover in genetic algorithms.
• Evolutionary algorithms in noisy environments: theoretical issues and guidelines for practise.
• Optimal Stabilization of Families of Linear Stochastic Differential Equations with Jump Coefficients and Multiplicative Noise
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