This paper analyzes the convergence rate and convergence for a class of genetic algorithms(GA’s) under elitist selection. The classification method about the state space is presented afterthe GA’s are described as a...This paper analyzes the convergence rate and convergence for a class of genetic algorithms(GA’s) under elitist selection. The classification method about the state space is presented afterthe GA’s are described as a Markov chain. It is proved by means of this method that the GA’s have ageometric convergence rate. The final part shows that the best solution in the population converges tothe global optimum with probability one.展开更多
文摘This paper analyzes the convergence rate and convergence for a class of genetic algorithms(GA’s) under elitist selection. The classification method about the state space is presented afterthe GA’s are described as a Markov chain. It is proved by means of this method that the GA’s have ageometric convergence rate. The final part shows that the best solution in the population converges tothe global optimum with probability one.
