A Contingent Searching for Improvement of Evolutions

Genetic algorithm (GA) technique is a promising approach to identify an optimum manufacturing cost by concurrently considering significant number of related variables. It is a superior technique for discovering optimal alternative solutions for multiple objective optimization problems. In GA, however, the setting of operators and the selections of their parameters could affect the output significantly, as they influence the processes of exploitation and exploration. The function of the dynamic setting is to adjust the trade-off between the exploration and exploitation of the search space. This research focuses on the application of evolution evidence to supervise the dynamic setting of the GA parameters. Theoretical test-beds were carried out to verify the theory. The practical experiments were conducted to solve the optimization problems in supply chain management. The experimental results have revealed that the dynamic setting improves the performance of supply chain management and the results also disclosed that dynamic is superior to static setting