Multistage Markov Chain Modeling of the Genetic Algorithm and Convergence Results

DOI10.1080/01630561003656268zbMath1202.90223OpenAlexW1987469847MaRDI QIDQ3578006

Judinor A. Jun. Guerra, R. Morgado, André G. C. Pereira, Chang Chung Yu Dorea

Publication date: 13 July 2010

Published in: Numerical Functional Analysis and Optimization (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/01630561003656268

zbMATH Keywords

global optimizationsimulated annealing

Mathematics Subject Classification ID

Approximation methods and heuristics in mathematical programming (90C59) Combinatorial optimization (90C27) Applications of Markov chains and discrete-time Markov processes on general state spaces (social mobility, learning theory, industrial processes, etc.) (60J20) Large deviations (60F10) Numerical methods for mathematical programming, optimization and variational techniques (65K99)

Related Items (3)

Unnamed Item ⋮ Multistage non homogeneous Markov chain modeling of the non homogeneous genetic algorithm and convergence results ⋮ Mean Convergence Time of Inhomogeneous Genetic Algorithm with Elitism

Cites Work

• Unnamed Item
• Sharp asymptotic results for simplified mutation-selection algorithms
• Sufficient Conditions for Ergodicity and Convergence of MH, SA, and EM Algorithms
• Asymptotic convergence of genetic algorithms
• Techniques for bounding the convergence rate of genetic algorithms

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