Evolving artificial neural networks

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

2265 Citations (Scopus)


Learning and evolution are two fundamental forms of adaptation. There has been a great interest in combining learning and evolution with artificial neural networks (ANN's) in recent years. This paper: 1) reviews different combinations between ANN's and evolutionary algorithms (EA's), including using EA's to evolve ANN connection weights, architectures, learning rules, and input features; 2) discusses different search operators which have been used in various EA's; and 3) points out possible future research directions. It is shown, through a considerably large literature review, that combinations between ANN's and EA's can lead to significantly better intelligent systems than relying on ANN's or EA's alone.
Original languageEnglish
Pages (from-to)1423-1447
Number of pages25
JournalProceedings of the IEEE
Issue number9
Publication statusPublished - 1999
Externally publishedYes

Bibliographical note

This work was supported in part by the Australian Research Council through its small grant scheme.


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