应用加速遗传算法解决组合证券投资决策问题，可以克服传统遗传算法的缺点：对搜索空间（优化变量空间）的大小变化适应能力差，计算量大，易出现早熟收敛，控制参数的设置技术无明确准则指导等，与已有结果相比，对协方差矩阵无正定性要求，目标函数可以推广到规模庞大，提高预测精度等优点。

It can win through traditional genetic algorithm's shortcomins by applying accelerating genetic algorithm in solving combination forecasting problems. These shortcomings include poor adapt ability in search space (i.e. optimizing variable space), large measure quantity, premature convergence, no definitude instruct rule for setting technique of control parameter, etc. The new approach does not need canonicity in forecasting error information matrix, the objective function scale may extend widely,and the forecasting precision is high.