An Improvement of Genetic Algorithm for Optimization Problem

This paper proposed an improvement of genetic algorithm for optimization problem. In this study, the Gaussian function is applied in crossover and mutation operators instead of traditional crossover and mutation. The algorithm is tested on five benchmark problems and compared with the self-adaptive DE algorithm, traditional differential evolution (DE) algorithm, the JDE self-adaptive algorithm and the hybrid bat algorithm with natural-inspired. The computation results illustrate that the proposed algorithm can produce optimal solutions for all functions. Comparing to the other four algorithms, the proposed algorithm provides the best results. The finding proves that the algorithm should be improved in this direction.