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Stochastic analysis and convergence velocity estimation of genetic algorithms 被引量:1
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作者 guo guan-qi(郭观七) yu shou-yi(喻寿益) 《Journal of Central South University of Technology》 2003年第1期58-63,共6页
Formulizations of mutation and crossover operators independent of representation of solutions are proposed. A kind of precisely quantitative Markov chain of populations of standard genetic algorithms is modeled. It is... Formulizations of mutation and crossover operators independent of representation of solutions are proposed. A kind of precisely quantitative Markov chain of populations of standard genetic algorithms is modeled. It is proved that inadequate parameters of mutation and crossover probabilities degenerate standard genetic algorithm to a class of random search algorithms without selection bias toward any solution based on fitness. After introducing elitist reservation, the stochastic matrix of Markov chain of the best-so-far individual with the highest fitness is derived.The average convergence velocity of genetic algorithms is defined as the mathematical expectation of the mean absorbing time steps that the best-so-far individual transfers from any initial solution to the global optimum. Using the stochastic matrix of the best-so-far individual, a theoretic method and the computing process of estimating the average convergence velocity are proposed. 展开更多
关键词 GENETIC algorithm OPERATOR formulization MARKOV CHAIN CONVERGENCE VELOCITY
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