Finite Markov chain analysis of genetic algorithms
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic programming (videotape): the movie
Genetic programming (videotape): the movie
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Induction: Processes of Inference, Learning, and Discovery
Induction: Processes of Inference, Learning, and Discovery
Global Convergence of Genetic Algorithms: A Markov Chain Analysis
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
A Genetic Model: Analysis and Application to MAXSAT
Evolutionary Computation
Modeling simple genetic algorithms
Evolutionary Computation
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Simple genetic algorithms on populations of l-binary words usually become iterative systems on 2l dimensional spaces when populations have size infinite. However, in a particular model (BCCG model) previously introduced, it has been shown that the iterative system works in a l-dimensional space. In this paper we propose a simplification of the BCCG model and we analyze it in the case of large but finite-size populations. In particular: We exhibit a Markov chain with states in ℝl that approximates the system behavior. We estimate the steady state distribution of the Markov chain.