Deakin University
Browse

File(s) under permanent embargo

Integration of supervised ART-based neural networks with a hybrid genetic algorithm

journal contribution
posted on 2011-02-01, 00:00 authored by S Tan, Chee Peng LimChee Peng Lim
In this paper, two evolutionary artificial neural network (EANN) models that are based on integration of two supervised adaptive resonance theory (ART)-based artificial neural networks with a hybrid genetic algorithm (HGA) are proposed. The search process of the proposed EANN models is guided by a knowledge base established by ART with respect to the training data samples. The EANN models explore the search space for “coarse” solutions, and such solutions are then refined using the local search process of the HGA. The performances of the proposed EANN models are evaluated and compared with those from other classifiers using more than ten benchmark data sets. The applicability of the EANN models to a real medical classification task is also demonstrated. The results from the experimental studies demonstrate the effectiveness and usefulness of the proposed EANN models in undertaking pattern classification problems.

History

Journal

Soft computing

Volume

15

Issue

2

Pagination

205 - 219

Publisher

Springer

Location

Heidelberg, Germany

ISSN

1432-7643

eISSN

1433-7479

Language

eng

Publication classification

C1.1 Refereed article in a scholarly journal

Copyright notice

2010, Springer-Verlag