Convergence of genetic evolution algorithms for optimization
作者： JUN HE , LI-SHAN KANG , YONG-JUN CHEN
DOI: 10.1080/10637199508915474
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摘要: Genetic algorithms are highly parallel, adaptive search method based on the processes of Darwinian evolution. This paper combines genetic with simulated annealing to a new kind random which is called evolution algorithms. We give some conditions guarantee converge global optima set probability 1 for solving optimization problems and analyze convergence by using Markov chain theory.
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