Parallelizing the Design of Radial Basis Function Neural Networks by Means of Evolutionary Meta-algorithms

Authors:
M. G. Arenas;E. Parras-Gutiérrez;V. M. Rivas;P. A. Castillo;M. J. Jesus;J. J. Merelo
Affiliations:
Department of Computer Architecture and Technology, CITIC-UGR, Granada, Spain 18071;Department of Computer Sciences, Jaen, Spain 23071;Department of Computer Sciences, Jaen, Spain 23071;Department of Computer Architecture and Technology, CITIC-UGR, Granada, Spain 18071;Department of Computer Sciences, Jaen, Spain 23071;Department of Computer Architecture and Technology, CITIC-UGR, Granada, Spain 18071
Venue:
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Year:
2009

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Abstract

This work introduces SymbPar, a parallel meta-evolutionary algorithm designed to build Radial Basis Function Networks minimizing the number of parameters needed to be set by hand. Parallelization is implemented using independent agents to evaluate every individual. Experiments over classifications problems show that the new method drastically reduces the time took by sequential algorithms, while maintaining the generalization capabilities and sizes of the nets it builds.