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• [P377] Improving Generalization of Radial Basis Function Network with Adaptive Multi-Objective Particle Swarm Optimization
  Author(s): Sultan Noman Qasem, Siti Mariyam Shamsuddin
  
  

  Abstract
  In this paper, an adaptive evolutionary multi-objective selection method of RBF Networks structure is discussed. The candidates of RBF Network structure are encoded into particles in PSO. These particles evolve toward Pareto-optimal front defined by several objective functions concerning with model accuracy and model complexity. The problem of unsupervised and supervised learning is discussed within the context of multi-objective optimization 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 UCI 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.

  
   

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