An Improvement of Genetic Algorithm with Rao Algorithm for Optimization Problems

Abstract

This paper proposes an improvement of genetic algorithm for optimization problems. In this study, the Rao algorithm was applied in crossover and mutation operators instead of traditional crossover and mutation. The algorithm was tested on six benchmark problems and compared with differential evolution (DE), JDE self-adaptive algorithm, and intersection mutation differential evolution (IMDE) algorithm. The computation results illustrated that the proposed algorithm can produce optimal solutions for three of six functions. Comparing to the other three algorithms, the proposed algorithm has provided the best results. The findings prove that the algorithm should be improved in this direction and show that the algorithm produces several solutions obtained by the previously published methods, especially for the continuous step function, the multimodal function and the discontinuous step function.