Abstract
Several gradient-based methods have been developed for Artificial Neural Network (ANN) training. Still, in some situations, such procedures may lead to local minima, making Evolutionary Algorithms (EAs) a promising alternative. In this work, EAs using direct representations are applied to several classification and regressionANN learning tasks. Furthermore, EAs are also combined with local optimization, under the Lamarckian framework. Both strategies are compared with conventional training methods. The results reveal an enhanced performance by a macro-mutation based Lamarckian approach.
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References
Belew, R., McInerney, J., Schraudolph, N.: Evolving Networks: Using the Genetic Algorithms with Connectionist Learning. CSE TR CS90-174, UCSD (1990)
Bishop, C.: Neural Networks for Pattern Recognition. Oxford Univ. Press, Oxford (1995)
Blake, C., Merz, C.: UCI Repository of Machine Learning Databases (1998)
Fogel, L.J.: Intelligence Through Simulated Evolution: Forty Years of Evolutionary Programming. John Wiley, New York (1999)
Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs, 3rd edn. Springer, USA (1996)
Montana, D., Davis, L.: Training feedforward neural networks using genetic algorithms. In: Proc. 11th IJCAI, pp. 762–767. Morgan Kaufmann, San Francisco (1989)
Riedmiller, M.: Supervised Learning in Multilayer Perceptrons – from Backpropagation to Adaptive Learning Techniques. Comp. Stand. and Interfaces, 16 (1994)
Yao, X.: Evolving Artificial Neural Networks. Proc. IEEE 87(9), 1423–1447 (1999)
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© 2003 Springer-Verlag Berlin Heidelberg
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Rocha, M., Cortez, P., Neves, J. (2003). Evolutionary Neural Network Learning. In: Pires, F.M., Abreu, S. (eds) Progress in Artificial Intelligence. EPIA 2003. Lecture Notes in Computer Science(), vol 2902. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24580-3_10
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DOI: https://doi.org/10.1007/978-3-540-24580-3_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20589-0
Online ISBN: 978-3-540-24580-3
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