Simultaneous Evolution of Network Architectures and Connection Weights in Artificial Neural Networks

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Author(s): Ruhul A. Sarker (University of New South Wales, Australia)and Hussein A. Abbass (University of New South Wales, Australia)
Copyright: 2006
Pages: 15
Source title: Artificial Neural Networks in Finance and Manufacturing
Source Author(s)/Editor(s): Joarder Kamruzzaman (Monash University, Australia), Rezaul Begg (Victoria University, Australia)and Ruhul Sarker (University of New South Wales, Australia)
DOI: 10.4018/978-1-59140-670-9.ch002

Keywords: Business Science Reference / Computer Science & IT / Industrial Informatics / Neural Networks

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Abstract

Artificial Neural Networks (ANNs) have become popular among researchers and practitioners for modeling complex real-world problems. One of the latest research areas in this field is evolving ANNs. In this chapter, we investigate the simultaneous evolution of network architectures and connection weights in ANNs. In simultaneous evolution, we use the well-known concept of multiobjective optimization and subsequently evolutionary multiobjective algorithms to evolve ANNs. The results are promising when compared with the traditional ANN algorithms. It is expected that this methodology would provide better solutions to many applications of ANNs.

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Simultaneous Evolution of Network Architectures and Connection Weights in Artificial Neural Networks

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