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The problem of unsupervised and supervised learning is discussed within the context of multi-objective optimization. In this paper, an evolutionary multi-objective selection method of RBF networks structure is discussed. The candidates of RBF network structure are encoded into the particles in PSO. Then, they evolve toward Pareto-optimal front defined by several objective functions concerning with model accuracy and model complexity. This study suggests an approach of RBF network training through simultaneous optimization of architectures and weights with PSO-based multi-objective algorithm. Our goal is to determine whether multi-objective PSO can train RBF networks, and the performance is validated on accuracy and complexity. The experiments are conducted on 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 other evolutionary computational-based methods.