Genetic algorithm with forgetting and its application in initial credit scoring
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摘要: 為解決局部最優問題,將遺忘機制引入傳統遺傳算法中,提出了一種改進的遺忘遺傳算法,給出了一種遺忘算子及其遺忘概率,通過在遺傳過程中遺忘某些基因,增加了算法的搜索空間,使算法跳出局部最優,從而最大限度地避免早熟收斂.將該算法用于不同欠費率下的電信客戶初始信用評分,找到信用權重的優化解,較好地解決了對高欠費率群體進行信用評分時,信用權重的適應值偏低的問題.實驗結果表明所提算法有效可行.與標準遺傳算法相比,本文所提算法可以獲得更高質量的解.Abstract: 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.
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Key words:
- genetic algorithms /
- forgetting factor /
- customer service /
- credit scoring
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