Enhanced genetic algorithm applied for global optimization

Abstract

Conventional genetic algorithm (GA) has several drawbacks such as premature convergence and incapable of fine tuning around potential region. Thus, new enhanced GA that focuses on new search, crossover and elitism strategy is proposed in this study. It involves solution enhancement phase by performing search among high quality chromosomes via new crossover operator. A modified elitism operation is devised to ensure that the performance of enhanced GA not getting worse than the standard GA in case of solution enhance phase fails to find better chromosomes. In modified elitism, best chromosomes resulted from the enhancement phase and normal population will have to compete among each other to survive in next generation. The enhanced GA has been applied for solving global optimization of benchmark test functions and compared with standard GA. Based on the occurrences of the algorithms produce the best result across different test functions and elitism size; it is proven that the proposed method outperforms standard GA.