Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
408374 | Neurocomputing | 2007 | 8 Pages |
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Miguel Rocha, Paulo Cortez, José Neves,