An improved genetic algorithm for Job-shop scheduling problem

Because selection, crossover, mutation were all random, they might destroy the present individual which had the best fitness, then impacted run efficiency and converge. So used the strategy reserve the best individual, then the average fitness of chromosomes was improved, and the loss of the best solution was prevented. At the same time introduced the probability of crossover and mutation based on fitness, then it enhanced the genetic algorithm´s evolution ability, and the speed of the evolution was increased. And we find it is effective when solve the Job-shop scheduling problem.