Towards designing artificial neural networks by evolution

Xin YAO, Yong LIU

Research output: Journal PublicationsJournal Article (refereed)peer-review

114 Citations (Scopus)


Designing artificial neural networks (ANNs) for different applications has been a key issue in the ANN field. At present, ANN design still relies heavily on human experts who have sufficient knowledge about ANNs and the problem to be solved. As ANN complexity increases, designing ANNs manually becomes more difficult and unmanageable. Simulated evolution offers a promising approach to tackle this problem. This paper describes an evolutionary approach to design ANNs. The ANNs designed by the evolutionary process are referred to as evolutionary ANNs (EANNs). They represent a special class of ANNs in which evolution is another fundamental form of adaptation in addition to learning (also known as weight training). This paper describes an evolutionary programming (EP) based system to evolve both architectures and connection weights (including biases) of ANNs. Five mutation operators have been proposed in our evolutionary algorithm. In order to improve the generalisation ability of evolved ANNs, these five operators are applied sequentially and selectively. Validation sets have also been used in the evolutionary process in order to improve generalisation further. The evolutionary algorithm allows ANNs to grow as well as shrink during the evolutionary process. It incorporates the weight learning process as part of its mutation process. The whole EANN system can be regarded as a hybrid evolution and learning system. Extensive experimental studies have been carried out to test this EANN system. This paper gives some of the experimental results which show the effectiveness of the system. © 1998 Elsevier Science Inc. All rights reserved.
Original languageEnglish
Pages (from-to)83-90
Number of pages8
JournalApplied Mathematics and Computation
Issue number1
Publication statusPublished - Apr 1998
Externally publishedYes

Bibliographical note

This work is supported by the Australian Research Council through its small grant scheme.


  • Adaptation
  • Artificial neural networks
  • Evolution
  • Evolutionary algorithms
  • Generalisation
  • Learning


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