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R. Sikora and S. Piramuthu, “Framework for efficient feature selection in genetic algorithm based data mining,” European Journal of Operational Research, Elsevier Science Inc., Vol. 180, pp. 723-737, 2007.

has been cited by the following article:

• TITLE: Research on Financial Distress Prediction with Adaptive Genetic Fuzzy Neural Networks on Listed Corporations of China

  AUTHORS: Zhibin XIONG

  KEYWORDS: Multi-Population Adaptive Genetic BP Algorithm, Fuzzy Neural Network, Cross Validation, Financial Distress

  JOURNAL NAME: International Journal of Communications, Network and System Sciences, Vol.2 No.5, August 21, 2009

  ABSTRACT: To design a multi-population adaptive genetic BP algorithm, crossover probability and mutation probability are self-adjusted according to the standard deviation of population fitness in this paper. Then a hybrid model combining Fuzzy Neural Network and multi-population adaptive genetic BP algorithm—Adaptive Genetic Fuzzy Neural Network (AGFNN) is proposed to overcome Neural Network’s drawbacks. Furthermore, the new model has been applied to financial distress prediction and the effectiveness of the proposed model is performed on the data collected from a set of Chinese listed corporations using cross validation approach. A comparative result indicates that the performance of AGFNN model is much better than the ones of other neural network models.