用Ｃ语言实现了遗传基因算法求解多变量优化问题，在此基础上，对单点交叉与多点交叉、单点变异与多点变异进行了比较研究．在采用较好的交叉和变异方法的基础上，研究了在ＧＡｓ的迭代循环中如何适时进行区域收缩，引入适值调整，找到了一种循环次数少、计算时间短、求解结果令人满意的ＧＡｓ算法 Using C language to solve the problem of multivariate optimization with genetic algorithm, on the basis of which, the single point crossover and multipoint crossover, single point mutation and multiple point mutation were compared. Based on the better crossover and mutation methods, this paper studies how to conduct regional contraction in GAs’ iteration cycle timely, introduces fitness adjustment and finds a GAs with less cycles, shorter computation time and satisfactory results algorithm