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).
Original language | English (US) |
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Pages (from-to) | 361-374 |
Number of pages | 14 |
Journal | Omega |
Volume | 29 |
Issue number | 4 |
DOIs | |
State | Published - Aug 1 2001 |
All Science Journal Classification (ASJC) codes
- Strategy and Management
- Management Science and Operations Research
- Information Systems and Management