The influence of diversity in an immune-based algorithm to train MLP networks

Authors:
Rodrigo Pasti;Leandro Nunes de Castro
Affiliations:
State University of Campinas;Catholic University of Santos
Venue:
ICARIS'07 Proceedings of the 6th international conference on Artificial immune systems
Year:
2007

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Abstract

This paper has three main goals: i) to employ an immune-based algorithm to train multi-layer perceptron (MLP) neural networks for pattern classification; ii) to combine the trained neural networks into ensembles of classifiers; and iii) to investigate the influence of diversity in the classification performance of individual and ensembles of classifiers. Two different classes of algorithms to train MLP are tested: bio-inspired, and gradient-based. Comparisons among all the training methods are presented in terms of classification accuracy and diversity of the solutions found.