Research and Application of Genetic Algorithm Based on Variable Crossover Probability

Abstract

Flow-Shop scheduling is a classic problem which belongs to combinatorial optimization problem, and belongs to NP-C problem. Basic Algorithm which simulates the evolution process is used widely in solving Flow-shop scheduling. Basic Genetic Algorithm used fix crossover probability and mutation probability during all the evolution process, if the probability is higher, It maybe destroy the population quantity at the ending of evolution process, and result in the convergence speed becomes slower. If the probability is lower, It maybe result in local optimization after finishing the evolution process. In this paper, we use the Genetic Algorithm which crossover probability is dynamically adjusted according to the individual's fitness value. The computational result shows that the performance of variable crossover probability Genetic Algorithm is better than Basic Genetic Algorithm.