Evolution of neural networks for classification and regression

Authors:
Miguel Rocha;Paulo Cortez;José Neves
Affiliations:
Dep. of Informatics, University of Minho, 4710-057 Braga, Portugal;Dep. of Information Systems, University of Minho, 4800-058 Guimarães, Portugal;Dep. of Informatics, University of Minho, 4710-057 Braga, Portugal
Venue:
Neurocomputing
Year:
2007

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Abstract

Although Artificial Neural Networks (ANNs) are important data mining techniques, the search for the optimal ANN is a challenging task: the ANN should learn the input-output mapping without overfitting the data and training algorithms may get trapped in local minima. The use of Evolutionary Computation (EC) is a promising alternative for ANN optimization. This work presents two hybrid EC/ANN algorithms: the first evolves neural topologies while the latter performs simultaneous optimization of architectures and weights. Sixteen real-world tasks were used to test these strategies. Competitive results were achieved when compared with a heuristic model selection and other Data Mining algorithms.