Convergent Rate of Genetic Algorithms with Arbitrary Encoding
n 进制编码遗传算法的收敛速度

At present,any mode for studying the convergence of genetic algorithm is limit. Thus,it is important and significant to study the convergence of genetic algorithms from another viewpoint,which help build right stopping criteria and appropriate measure one for comprehensively and unbiasedly judging any algorithm.However,there are few results about convergence rate.Moreover,nearly all these results limit to binary encoding genetic algorithms.In this paper,an upper bound on the rates of convergence to distributions of canonical genetic algorithms with arbitrary encoding is presented by using a special minorization condition,and effects of the population size,the encoding strength and the mutation probability on the convergence rate are given.It generalizes the existing results,and is of referenced value for designing algorithms.