Improving generalization of radial basis function network with adaptive multi-objective particle swarm optimization

Authors:
Sultan Noman Qasem;Siti Mariyam Hj. Shamsuddin
Affiliations:
Faculty of Computer Science and Information System, Universiti Teknologi Malaysia, Johor, Malaysia;k-Economy Research Alliance, Universiti Teknologi Malaysia, Johor, Malaysia
Venue:
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Year:
2009

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Abstract

In this paper, an adaptive evolutionary multi-objective selection method of RBF Networks structure is discussed. The candidates of RBF Network structures are encoded into particles in Particle Swarm Optimization (PSO). These particles evolve toward Pareto-optimal front defined by several objective functions with model accuracy and complexity. The problem of unsupervised and supervised learning is discussed with Adaptive Multi-Objective PSO (AMOPSO). This study suggests an approach of RBF Network training through simultaneous optimization of architectures and weights with Adaptive PSO-based multi-objective algorithm. Our goal is to determine whether Adaptive Multi-objective PSO can train RBF Networks, and the performance is validated on accuracy and complexity. The experiments are conducted on two benchmark datasets obtained from the machine learning repository. The results show that our proposed method provides an effective means for training RBF Networks that is competitive with PSO-based multi-objective algorithm.