Title

Mo-Gart: Multiobjective Genetic Art Architectures

Abstract

In this work we present, for the first time, the evolution of ART Neural Network architectures (classifiers) using a multiobjective optimization approach. In particular, we propose the use of a multiobjective evolutionary approach to evolve simultaneously the weights, as well as the topology of three well-known ART architectures; Fuzzy ARTMAP (FAM), Ellipsoidal ARTMAP (EAM) and Gaussian ARTMAP (GAM). We refer to the resulting architectures as MO-GFAM, MO-GEAM, or MO-GGAM, and collectively as MO-GART. The major advantage of MO-GART is that it produces a number of solutions for the classification problem at hand that have different levels of merit (accuracy on unseen data (generalization) and size (number of categories created)). MO-GART is shown to be more elegant (does not require user intervention to define the network parameters), more effective (of better accuracy and smaller size), and more efficient (faster to produce the solution networks) than other ART neural network architectures that have appeared in the literature. © 2008 IEEE.

Publication Date

11-14-2008

Publication Title

2008 IEEE Congress on Evolutionary Computation, CEC 2008

Number of Pages

1425-1432

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/CEC.2008.4630981

Socpus ID

55749105687 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/55749105687

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