Abstract

One of the major issues in the field of artificial neural networks (ANNs) is the design of their architectures. There are strong biological and engineering evidences to support that the information processing capability of an ANN is determined by its architecture. This paper proposes a new population-based learning algorithm (PBLA) which learns both ANN's architecture and weights. The evolutionary approach is used to evolve a population of ANNs. Unlike other evolutionary approaches to ANN learning, each ANN (i.e., individual) in the population is evaluated by partial training rather than complete training. Substantial savings in computational cost can be achieved by such progressive partial training. This training process can change both ANN's architecture and weights. Our preliminary experiments have demonstrated the effectiveness of our algorithm. 1 Introduction One of the major issues in the field of ANNs is the design of their architectures. There are strong biological and enginee...

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