Using ensemble information in swarming artificial neural networks

  • Authors:

  • Jian Tang;Zengqi Sun;Jihong Zhu

  • Affiliations:

  • State Key Laboratory of Intelligent Technology and Systems, Department of Computer Science and Technology, Tsinghua University, Beijing, China;State Key Laboratory of Intelligent Technology and Systems, Department of Computer Science and Technology, Tsinghua University, Beijing, China;State Key Laboratory of Intelligent Technology and Systems, Department of Computer Science and Technology, Tsinghua University, Beijing, China

  • Venue:
  • ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
  • Year:
  • 2005

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Abstract

Artificial neural network (ANN) ensembles are effective techniques to improve the generalization of a neural network system. This paper presents an evolutionary approach to train feedforward neural networks with Particle Swarm Optimization (PSO) algorithm, then the swarming neural networks are organized as an ensemble to give a combined output. Three real-world data sets have been used in our experimental studies, which show that the fitness-based congregate ensemble usually outperforms the best individual. The results confirm that PSO is a rapid promising evolutionary algorithm, and evolutionary learning should exploit collective information to improve generalization of learned systems.