Artificial Neural Networks (ANNs) are important Data Mining (DM) techniques. Yet, the search for the optimal ANN is a challenging task: the architecture should learn the input-output mapping without overfitting the data and training algorithms tend to get trapped into local minima. Under this scenario, the use of Evolutionary Computation (EC) is a promising alternative for ANN design and training. Moreover, since EC methods keep a pool of solutions, an ensemble can be build by combining the best ANNs. This work presents a novel algorithm for the optimization of ANNs, using a direct representation, a structural mutation operator and Lamarckian evolution. Sixteen real-world classification/regression tasks were used to test this strategy with single and ensemble based versions. Competitive results were achieved when compared with a heuristic model selection and other DM algorithms. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Rocha, M., Cortez, P., & Neves, J. (2005). Simultaneous evolution of neural network topologies and weights for classification and regression. In Lecture Notes in Computer Science (Vol. 3512, pp. 59–66). Springer Verlag. https://doi.org/10.1007/11494669_8
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