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Journal of Automation and Information Sciences

Published 12 issues per year

ISSN Print: 1064-2315

ISSN Online: 2163-9337

SJR: 0.173 SNIP: 0.588 CiteScore™:: 2

Indexed in

Coevolving Feedforward Neural Networks

Volume 48, Issue 9, 2016, pp. 36-48
DOI: 10.1615/JAutomatInfScien.v48.i9.30
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ABSTRACT

An evolutionary algorithm of determining the architecture of feedforward neural networks and their training is proposed, based on the coevolutionary models of cooperation and competition with using of clustering algorithms for partitioning the main problem of neural network synthesis into subtasks which are to be solved in certain sub-populations. The proposed algorithm implements an environment that is conducive to cooperation and competition of populations in which every individual is a feedforward neural network, and the totality of the populations is responsible for the final solution of the set problem. The simulation results confirm the effectiveness of the proposed method of feedforward neural networks synthesis.

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