Introduction - If you have any usage issues, please Google them yourself
Works by simulating the process of biological evolution in nature, design corresponding evolution operators and operations, to solve complex practical problems, is a search optimization algorithm based on natural selection and genetic basis. It is a group of the population randomly generated the beginning of this population by a gene coding for a certain number of individual composition, in accordance with the rules of survival of the fittest and survival of the fittest, by comparing the size of each individual fitness, choose the larger individuals fitness crossover and mutation, a new generation of better adapted populations produce environmental participate evolution. Through continuous breeding generations of evolution, finally get most individuals adapt to the environment, to obtain the optimal solution.