Evolving an Ensemble of Neural Networks Using Artificial Immune Systems

Authors:
Bruno H. Barbosa;Lam T. Bui;Hussein A. Abbass;Luis A. Aguirre;Antônio P. Braga
Affiliations:
Department of Electronic Engineering, Federal University of Minas Gerais, Belo Horizonte, Brazil and School of Information Technology and Electrical Engineering, Australian Defence Force Academy, ...;School of Information Technology and Electrical Engineering, Australian Defence Force Academy, University of New South Wales, Canberra, Australia;School of Information Technology and Electrical Engineering, Australian Defence Force Academy, University of New South Wales, Canberra, Australia;Department of Electronic Engineering, Federal University of Minas Gerais, Belo Horizonte, Brazil;Department of Electronic Engineering, Federal University of Minas Gerais, Belo Horizonte, Brazil
Venue:
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
Year:
2008

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Abstract

This paper presents a novel ensemble construction approach based on Artificial Immune Systems (AIS) to solve regression problems. Over the last few years AIS have increasingly attracted interest from researchers due to their ability to balance the exploration and exploitation of the search space. Nevertheless, there have been just a few applications of those algorithms in the construction of committee machines. In this paper, a population of feed-forward neural networks is evolved using the Clonal Selection Algorithm and then ensembles are automatically composed of a subset of this neural network population. Results show that the proposed algorithm can achieve good generalization performance on some hard benchmark regression problems.