摘要
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 。
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. [
出处
《中国工程科学》
2002年第9期59-62,共4页
Strategic Study of CAE
基金
国家杰出青年科学基金资助项目 (7972 5 0 0 2 )
信息产业部软科学资助项目 (信产信科 [2 0 0 0 ] 8号 )