Abstract
In this paper I want to argue that the combination of evolutionary algorithms and neural networks can be fruitful in several ways. When estimating a functional relationship on the basis of empirical data we face three basic problems. Firstly, we have to deal with noisy and finite-sized data sets which is usually done be regularization techniques, for example Bayesian learning. Secondly, for many applications we need to encode the problem by features and have to decide which and how many of them to use. Bearing in mind the empty space phenomenon, it is often an advantage to select few features and estimate a non-linear function in a low-dimensional space. Thirdly, if we have trained several networks, we are left with the problem of model selection. These problems can be tackled by integrating several stochastic methods into an evolutionary search algorithm. The search can be designed such that it explores the parameter space to find regions corresponding to networks with a high posterior probability of being a model for the process, that generated the data. The benefits of the approach are demonstrated on a regression and a classification problem.
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References
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Alander, J. T. An Indexed Bibliography of Genetic Algorithms and Neural Networks. Technical Report 94-1-NN, Department of Information Technology and Production Economics, University of Vaasa, 1996.
Bishop, C. M. Neural Networks for Pattern Recognition. Oxford Press, 1995.
Braun, H. and Ragg, T. ENZO-Evolution of Neural Networks, User Manual and Implementation Guide, http://i11www.ira.uka.de. Technical Report 21/96, Universität Karlsruhe, 1996.
Braun, H. Neuronale Netze: Optimierung durch Lernen und Evolution. Springer, Heidelberg, 1997.
Büning, H. and Trenkler, G. Nichtparametrische statistische Methoden. de Gruyter, 1994.
Cover, T. and Thomas, J. Elements of Information Theory. Wiley Series in Telecommunications. John Wiley & Sons, 1991.
Fukunaga, K. Introduction to Statistical Pattern Recognition. Academic Press, 1990.
Gutjahr, S. Improving the determination of the hyperparameters in bayesian learning. In Downs, T., Frean, M., and Gallagher, M., editors, Proceedings of the Ninth Australian Conference on Neural Networks (ACNN 98), pages 114–118, Brisbane, Australien, 1998.
Gutjahr, S. Optimierung Neuronaler Netze mit der Bayes’schen Methode. Dissertation, Universität Karlsruhe, Institut für Logik, Komplexität und Deduktionssysteme, 1999.
[Hertz et al., 1991]_ Hertz, J., Krough, A., and Palmer, R. G. Introduction to the theory of neural computation, volume 1 of Santa Fe Institute, Studies in the sciences of complexity, lecture notes. Addison-Wesley, 1991.
Jeffreys, H. Theory of Probability. Oxford University Press, 1961.
Krogh, A. and Hertz, J. A Simple Weight Decay Can Improve Generalisation. In Advances in Neural Information Processing 4, pages 950–958,1992.
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Ragg, T. (2001). Bayesian Learning and Evolutionary Parameter Optimization. In: Baader, F., Brewka, G., Eiter, T. (eds) KI 2001: Advances in Artificial Intelligence. KI 2001. Lecture Notes in Computer Science(), vol 2174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45422-5_5
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DOI: https://doi.org/10.1007/3-540-45422-5_5
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