Harnessing mutational diversity at multiple levels for improving optimization accuracy in G3-PCX

The objective of this paper is to implement a multi- pronged strategy for generating diversity using non-adaptive, adaptive as well as self-adaptive methods for controlling mutation operations in a real-coded genetic algorithm (RCGA). Currently, one of the state-of-the-art RCGAs for function optimization is called the G3-PCX algorithm. However, its performance for solving multimodal problems is known to be poor compared with its performance for unimodal problems. In G3-PCX, the main problem primarily stems from premature convergence to local rather than global optima due to lack of explorative capabilities of the algorithm. As the G3-PCX algorithm relies completely on crossover for promoting diversity, this paper proposes a multilevel mutation operator to augment the algorithm´s capability of escaping local optima. The proposed algorithm is called G3M2 (G3-PCX with Multilevel Mutation) and empirical tests on four benchmark multimodal test functions have shown highly competitive performance. The objective of this paper is to investigate whether the proposed multilevel mutation is able to improve the precision accuracy of G3-PCX in solving multimodal function optimization problems. In three of the four problems, G3M2 outperformed the standard G3-PCX algorithm in terms of solution quality. Thus, the multilevel combination of non-adaptive, adaptive and self-adaptive parameter control strategies into a single paradigm is empirically shown to have beneficial effects for enhancing the effectiveness of the G3-PCX algorithm for solving multimodal optimization problems in terms of solution quality.