Crossover is an important process in genetic algorithms. This process will swap genes between the chromosomes of the parents. The results from the crossover process may not be better than those of the parents, which affect the result of the genetic algorithm. We are interested in considering whether we should or should not crossover by checking the results before making a decision to crossover. If the result of the crossover from checking is not better than the parents, we do not crossover, but if the results of the crossover from checking are better than the parents, we do crossover. In the results from the test with one maximum problem in different lengths, where the size of the population is 100, the size of the generation is 100, the crossover rate is 0.7, and the mutation rate is 0.10, we found that the results from crossover consideration were better than those from a simple genetic algorithm in all lengths tested. In future work, we will apply this methodology to real-world problems.
原文链接
分享
引用
