Abstract
In this paper we present a new method for hybrid evolutionary algorithms where only a few best individuals are subject to local optimization. Moreover, the optimization algorithm is only applied at specific stages of the evolutionary process. The key aspect of our work is the use of a clustering algorithm to select the individuals to be optimized. The underlying idea is that we can achieve a very good performance if, instead of optimizing many very similar individuals, we optimize just a few different individuals. This approach is less computationally expensive. Our results show a very interesting performance when this model is compared to other standard algorithms. The proposed model is evaluated in the optimization of the structure and weights of product-unit based neural networks.
This work has been financed in part by the TIC2002-04036-C05-02 project of the Spanish Inter-Ministerial Commission of Science and Technology (CICYT) and FEDER funds.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Houck, C.R., Joines, J.A., Kay, M.G.: Empirical investigation of the benefits of partial lamarckianism. Evolutionary Computation 5(1), 31–60 (1997)
Moscató, P., Cotta, C.: A gentle introduction to memetic algorithms. In: Glover, F., Kochenberger, G. (eds.) Handbook of Metaheuristics, pp. 1–56. Kluwer, Dordrecht (1999)
Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1995)
Durbin, R., Rumelhart, D.: Product units: A computationally powerful and biologically plausible extension to backpropagation networks. Neural Computation 1, 133–142 (1989)
Ismail, A., Engelbrecht, A.P.: Training product units in feedforward neural networks using particle swarm optimisation. In: Bajic, V.B., Sha, D. (eds.) Development and Practice of Artificial Intelligence Techniques, Proceeding of the International Conference on Artificial Intelligence, Durban, South Africa, pp. 36–40 (1999)
Leerink, L.R., Giles, C.L., Horne, B.G., Jabri, M.A.: Learning with product units. In: Advances in Neural Information Processing Systems, vol. 7, pp. 537–544. MIT Press, Cambridge (1995)
Saito, K., Nakano, R.: Extracting regression rules from neural networks. Neural Networks 15(10), 1279–1288 (2002)
Angeline, P.J., Saunders, G.M., Pollack, J.B.: An evolutionary algorithm that constructs recurrent neural networks. IEEE Transactions on Neural Networks 5(1), 54–65 (1994)
Martínez, A., Martínez, F., Hervás, C., García, N.: Model Fitting by evolutionary computation using product units. Neural Networks (2004) (submitted)
Fukunaga, K.: Introduction to Statistical Pattern Recognition. Academic Press, London (1990)
Friedman, J.: Multivariate adaptive regression splines (with discussion). Ann. Stat. 19, 1–41 (1991)
Carney, J., Cunningham, P.: Tuning diversity in bagged ensembles. Int. J. Neural Systems 10(4), 267–279 (2000)
Lee, W.M., Lim, C.P., Yuen, K.K., Lo, S.M.: A hybrid neural network model for noisy data regression. IEEE Transactions on Systems, Man and Cybernetics, Part B 34(2), 951–960 (2004)
Kosinski, W., Weigl, M.: Mapping neural networks and fuzzy inference systems for approximation of multivariate function. In: Kacki, E. (ed.) System Modelling Control, Artificial Neural networks and Their Applications, Lodz, Poland, May 1995, vol. 3, pp. 60–65 (1995)
Jankowski, N.: Approximation with RBF-type neural networks using flexible local and semi-local transfer functions. In: 4th Conference on Neural Networks and Their Applications, Zakopane, Poland, May 1999, pp. 77–82 (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Martínez-Estudillo, F., Hervás-Martínez, C., Martínez-Estudillo, A., Ortíz-Boyer, D. (2005). Memetic Algorithms to Product-Unit Neural Networks for Regression. In: Cabestany, J., Prieto, A., Sandoval, F. (eds) Computational Intelligence and Bioinspired Systems. IWANN 2005. Lecture Notes in Computer Science, vol 3512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494669_11
Download citation
DOI: https://doi.org/10.1007/11494669_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26208-4
Online ISBN: 978-3-540-32106-4
eBook Packages: Computer ScienceComputer Science (R0)