遗忘遗传算法及其在信用评分中的应用

为解决局部最优问题,将遗忘机制引入传统遗传算法中,提出了一种改进的遗忘遗传算法,给出了一种遗忘算子及其遗忘概率,通过在遗传过程中遗忘某些基因,增加了算法的搜索空间,使算法跳出局部最优,从而最大限度地避免早熟收敛.将该算法用于不同欠费率下的电信客户初始信用评分,找到信用权重的优化解,较好地解决了对高欠费率群体进行信用评分时,信用权重的适应值偏低的问题.实验结果表明所提算法有效可行.与标准遗传算法相比,本文所提算法可以获得更高质量的解.

Genetic algorithm with forgetting and its application in initial credit scoring

Based on the forgetting strategy,an improved genetic algorithm was proposed to solve the problem of local optimization,and a forgetting operator as well as its forgetting probability was given.For the search space was increased by forgetting some genes during the period of inheritance,the algorithm can break away from local optimization and avoid the premature convergence to the greatest extent.By using the algorithm to deal with the credit scoring of telecom customers for different arrears rates,the optimum solution of credit weights in the case of high rate of arrears was found,so it solves the problem that the fitness of credit weights is low for the credit scoring of telecom customers in high arrears rates.Experimental results demonstrate that the algorithm is effective and feasible.Compared with the standard genetic algorithm,the proposed algorithm can obtain better quality results.