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Journal of Automation and Information Sciences
SJR: 0.232 SNIP: 0.464 CiteScore™: 0.27

ISSN Druckformat: 1064-2315
ISSN Online: 2163-9337

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Journal of Automation and Information Sciences

DOI: 10.1615/JAutomatInfScien.v48.i9.30
pages 36-48

Coevolving Feedforward Neural Networks

Oleg G. Rudenko
Kharkov National University of Radio and Electronics, Kharkov
Alexander A. Bezsonov
Kharkov National University of Radio and Electronics, Kharkov

ABSTRAKT

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.