Abstract
Radial Basis Function Neural Networks (RBFNNs) have been widely used to solve classification and regression tasks providing satisfactory results. The main issue when working with RBFNNs is how to design them because this task requires the optimization of several parameters such as the number of RBFs, the position of their centers, and their radii. The problem of setting all the previous values presents many local minima so Evolutionary Algorithms (EAs) are a common solution because of their capability of finding global minima. Two of the most important elements in an EAs are the crossover and the mutation operators. This paper presents a comparison between a non distributed multiobjective algorithm against several parallel approaches that are obtained by the specialisation of the crossover and mutation operators in different islands. The results show how the creation of specialised islands that use different combinations of crossover and mutation operators could lead to a better performance of the algorithm by obtaining better solutions.
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
Park, J., Sandberg, J.W.: Universal approximation using radial basis functions network. Neural Computation 3, 246–257 (1991)
Rojas, I., Anguita, M., Prieto, A., Valenzuela, O.: Analysis of the operators involved in the definition of the implication functions and in the fuzzy inference proccess. International Journal of Approximate Reasoning 19, 367–389 (1998)
Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evolutionary Computation 6(2), 182–197 (2002)
Tang, Y., Reed, P., Wagene, T.: How effective and efficient are multiobjective evolutionary algorithms at hydrologic model calibration?. Hydrology and Earth System Sciences 10, 289–307 (2006)
Guillén, A., Rojas, I., González, J., Pomares, H., Herrera, L., Valenzuela, O., Prieto, A.: A Possibilistic Approach to RBFN Centers Initialization. In: Ślęzak, D., Yao, J., Peters, J.F., Ziarko, W., Hu, X. (eds.) RSFDGrC 2005. LNCS (LNAI), vol. 3642, pp. 174–183. Springer, Heidelberg (2005)
Guillén, A., Rojas, I., González, J., Pomares, H., Herrera, L.J., Valenzuela, O., Prieto, A.G.: Improving Clustering Technique for Functional Approximation Problem Using Fuzzy Logic: ICFA Algorithm. In: Cabestany, J., Prieto, A.G., Sandoval, F. (eds.) IWANN 2005. LNCS, vol. 3512, pp. 272–279. Springer, Heidelberg (2005)
Marquardt, D.W.: An Algorithm for Least-Squares Estimation of Nonlinear Inequalities. SIAM J. Appl. Math. 11, 431–441 (1963)
Hancock, P.J.B.: Genetic Algorithms and Permutation Problems: a Comparison of Recombination Operators for Neural Net Structure Specification. In: Whitley, D. (ed.) Proceedings of COGANN workshop, IJCNN, Baltimore, IEEE Computer Society Press, Los Alamitos (1992)
Herrera, F., Lozano, M., Verdegay, J.L.: Tackling real-coded genetic algorithms: operators and tools for the behavioural analysis. Artificial Intelligence Reviews 12(4), 265–319 (1998)
Knowles, J., Corne, D.: On metrics for comparing non-dominated sets. In: Congress on Evolutionary Computation, CEC 2002 (2002)
van Veldhuizen, D.A., Zydallis, J.B., Lamont, G.B.: Considerations in engineering parallel multiobjective evolutionary algorithms. IEEE Trans. Evolutionary Computation 7(2), 144–173 (2003)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Guillén, A., Rojas, I., González, J., Pomares, H., Herrera, L.J., Paechter, B. (2007). Boosting the Performance of a Multiobjective Algorithm to Design RBFNNs Through Parallelization. In: Beliczynski, B., Dzielinski, A., Iwanowski, M., Ribeiro, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2007. Lecture Notes in Computer Science, vol 4431. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71618-1_10
Download citation
DOI: https://doi.org/10.1007/978-3-540-71618-1_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71589-4
Online ISBN: 978-3-540-71618-1
eBook Packages: Computer ScienceComputer Science (R0)