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

Authors:
Shing Chiang Tan;Chee Peng Lim
Affiliations:
Multimedia University, Faculty of Information Science and Technology, Melaka Campus, Jalan Ayer Keroh Lama, Bukit Beruang, 75450, Melaka, Malaysia;University of Science Malaysia, School of Electrical and Electronic Engineering, Engineering Campus, Nibong Tebal, 14300, Penang, Malaysia
Venue:
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Year:
2011

Citing 0
Cited 2

A clustering algorithm for radial basis function neural network initialization

Neurocomputing
A Modified micro Genetic Algorithm for undertaking Multi-Objective Optimization Problems

Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Recent Advances in Soft Computing: Theories and Applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.