Quantum-based algorithm for optimizing artificial neural networks

Tzyy Chyang Lu, Gwo Ruey Yu, Jyh-Chin Juang

Research output: Contribution to journalArticlepeer-review

31 Citations (Scopus)

Abstract

This paper presents a quantum-based algorithm for evolving artificial neural networks (ANNs). The aim is to design an ANN with few connections and high classification performance by simultaneously optimizing the network structure and the connection weights. Unlike most previous studies, the proposed algorithm uses quantum bit representation to codify the network. As a result, the connectivity bits do not indicate the actual links but the probability of the existence of the connections, thus alleviating mapping problems and reducing the risk of throwing away a potential candidate. In addition, in the proposed model, each weight space is decomposed into subspaces in terms of quantum bits. Thus, the algorithm performs a region by region exploration, and evolves gradually to find promising subspaces for further exploitation. This is helpful to provide a set of appropriate weights when evolving the network structure and to alleviate the noisy fitness evaluation problem. The proposed model is tested on four benchmark problems, namely breast cancer and iris, heart, and diabetes problems. The experimental results show that the proposed algorithm can produce compact ANN structures with good generalization ability compared to other algorithms.

Original languageEnglish
Article number6507335
Pages (from-to)1266-1278
Number of pages13
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume24
Issue number8
DOIs
Publication statusPublished - 2013 Aug 5

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence

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