An empirical study of design and testing of hybrid evolutionary-neural approach for classification

Parag C. Pendharkar

Omega, 2001, vol. 29, issue 4, 361-374

Abstract: We propose a hybrid evolutionary-neural approach for binary classification that incorporates a special training data over-fitting minimizing selection procedure for improving the prediction accuracy on holdout sample. Our approach integrates parallel global search capability of genetic algorithms (GAs) and local gradient-descent search of the back-propagation algorithm. Using a set of simulated and real life data sets, we illustrate that the proposed hybrid approach fares well, both in training and holdout samples, when compared to the traditional back-propagation artificial neural network (ANN) and a genetic algorithm-based artificial neural network (GA-ANN).

Keywords: Artificial; intelligence; Artificial; neural; networks; Genetic; algorithms; Discriminant; analysis; Classification; problem; Learning (search for similar items in EconPapers)
Date: 2001
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Citations: View citations in EconPapers (5)

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