Evolution of neural networks for classification and regression
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 Computati...
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| Outros Autores: | , |
| Formato: | article |
| Idioma: | eng |
| Publicado em: |
2007
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| Assuntos: | |
| Texto completo: | http://hdl.handle.net/1822/8028 |
| País: | Portugal |
| Oai: | oai:repositorium.sdum.uminho.pt:1822/8028 |
| Resumo: | 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. |
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