Tuning structural parameters of neural networks using genetic algorithm: A credit scoring application.

Neural networks (NNs) have successfully been applied to classification problems including credit scoring. The tuning of the structural parameters of the NNs has a direct impact on their accuracy. In this paper, a hybrid approach based on the genetic algorithm (GA) is proposed to adjust the structural parameters of a classifier NN to achieve high accuracy. Two well‐known credit scoring datasets—Australian and German datasets—are used to test the proposed approach. The results indicate that the proposed hybrid approach is able to successfully tune the structural parameters, while the accuracy of classification is enhanced and its complexity dramatically diminished in comparison with other existing approaches. The performance of the proposed algorithm has been investigated through statistical analysis The best‐known solutions achieved by the proposed approach have an accuracy equal to 97.78% and 87.1% for Australian and German datasets, respectively. The results indicate 2.68% and 0.1% improvement in comparison with the best results reported in the literature, respectively. This improvement is important for real cases in which millions of loans are allocated using credit scoring approaches. [ABSTRACT FROM AUTHOR]