An optimizer of Grey-Genetic Algorithms to improve the prediction efficiency for Taiwan import and export pollution

This study proposes an optimal controller of Grey-Genetic Algorithms (GGA) to improve the prediction efficiency of grey theory models (GM). In this research, we design the different experimental schemes for the improving efficiency of the prediction models. The simple model (GM(1, 1)) and multiple model (GM(1,N)) are applied in the short-lifecycle product prediction system that was presented and evaluated on their performances. These models GM(1,1); GM(1, N); RGM(1, 1); and RGM(1,N) are selected and simulated with those of the import and export data from Taiwan´s import & export manufacturers. The experimental results show that the optimal controller can improve the prediction accuracy of GM models. This controller mechanism of GGA´s model also offers the more effective simulation alternatives for increasing prediction accuracy by this proposed controller.