Performance of Beta Mutation on CEC 2017 and CEC 2022 Benchmarks

Yogesh Kumar, Kusum Deep

Abstract: Real Coded Genetic Algorithms (RCGAs) are pop-ular, versatile, non-traditional optimization techniques and can be applicable to a numerous variety of optimization problems. The performance of RCGAs depends on the choice of crossover and mutation operators as well as the selection operator. In order to improve the performance of RCGAs, it is necessary to develop new real-coded crossover operators and new real-coded mutation operators. In this research paper, a new real-coded mutation operator is proposed named Beta Mutation. The CEC 2017 and CEC 2022 benchmark problem sets are used to evaluate its performance. The results are compared with existing and well-known RCGAs. For evaluating its performance, the proposed mutation operator is compared with similar variants of RCGAs. Based on mean, standard deviation, best, worst of the objective function values, and Friedman's mean rank test. It is concluded that the proposed Beta Mutation operator outperforms the other mutation operator under investigation.