A genetic algorithm methodology for optimized harvesting sequence of sugarcane

New mathematical models capable of predicting sugarcane sugar content based on planting method and harvest time were used to develop a methodology for optimizing harvesting sequence, and harvesting time of sugarcane. The applicability of GA was discussed too. The methodology based on Genetic Algorithm (GA) was used to qualitatively analyze the determinant variables of planting mode to obtain optimized combination harvest sequence, harvest time and area of different planting methods under several setted planting mode transitions. Self-crossing, as opposed to conventional GA crossing methods, was used to retain the structural ratio of planting modes. In this study, first year harvest was used for the transition state and that of the second year for the steady-state runs, while the combination of the two were formed the objective function.