This paper discusses the convergence rates about a class of evolutionary algorithms in general search spaces by means of the ergodic theory in Markov chain and some techniques in Banach algebra.Under certain condition...This paper discusses the convergence rates about a class of evolutionary algorithms in general search spaces by means of the ergodic theory in Markov chain and some techniques in Banach algebra.Under certain conditions that transition probability functions of Markov chains corresponding to evolutionary algorithms satisfy,the authors obtain the convergence rates of the exponential order.Furthermore,they also analyze the characteristics of the conditions which can be met by genetic operators and selection strategies.展开更多
基金This work is supported by the National Natural Science Foundation of ChinaVisiting Scholar Foundation of Key Lab,in Univers
文摘This paper discusses the convergence rates about a class of evolutionary algorithms in general search spaces by means of the ergodic theory in Markov chain and some techniques in Banach algebra.Under certain conditions that transition probability functions of Markov chains corresponding to evolutionary algorithms satisfy,the authors obtain the convergence rates of the exponential order.Furthermore,they also analyze the characteristics of the conditions which can be met by genetic operators and selection strategies.
