A modified genetic algorithm based on the best schema and its application for function optimization

摘要

The genetic algorithm (GA) is a wildly employed evolutional algorithm in the field of combinatorial optimization. Criticism of this approach includes slow speed and premature result during the convergence procedure. Through introducing new crossover and mutation operators based on the best scheme, the paper proposes a more efficient method to improve its performance not only with quicker convergence speed but also with more opportunity to reach a global optimal value. Finally, the paper demonstrates its effectiveness by an example of a multi-peak function optimization problem.