Abstract
One of the main problems in the training of artificial neural networks is to define their initial weights and architecture. The use of evolutionary algorithms (EAs) to optimize artificial neural networks has been largely used because the EAs can deal with large, non-differentiable, complex and multimodal spaces, and because they are good in finding the optimal region. In this paper we propose the use of Adaptive Differential Evolution (JADE), a new evolutionary algorithm based in the differential evolution (DE), to deal with this problem of training neural networks. Experiments were performed to evaluate the proposed method using machine learning benchmarks for classification problems.
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da Silva, A.J., Mineu, N.L., Ludermir, T.B. (2010). Evolving Artificial Neural Networks Using Adaptive Differential Evolution. In: Kuri-Morales, A., Simari, G.R. (eds) Advances in Artificial Intelligence – IBERAMIA 2010. IBERAMIA 2010. Lecture Notes in Computer Science(), vol 6433. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16952-6_40
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DOI: https://doi.org/10.1007/978-3-642-16952-6_40
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