Abstract
A new cooperative evolutionary model, called Symbiont, for evolving artificial neural networks is presented in this paper. This model is based on the idea of developing subnetworks, called nodules, that must cooperate to form a solution, instead of evolving a complete network. The performance of the model in solving two real-world problems of classification is compared with a multilayer perceptron trained using back-propagation. Symbiont has proved to show better generalization than the multilayer perceptron and to evolve smaller networks.
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García-Pedrajas, N., Hervás-Martínez, C., Muñoz-Pérez, J. (2002). SYMBIONT: A Cooperative Evolutionary Model for Evolving Artificial Neural Networks for Classification. In: Bouchon-Meunier, B., Gutiérrez-Ríos, J., Magdalena, L., Yager, R.R. (eds) Technologies for Constructing Intelligent Systems 2. Studies in Fuzziness and Soft Computing, vol 90. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1796-6_27
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DOI: https://doi.org/10.1007/978-3-7908-1796-6_27
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