Multiobjective Optimization of Evolving Feedforward Neural Networks

Oleg G. Rudenko; Alexander A. Bezsonov

doi:10.1615/JAutomatInfScien.v46.i11.20

Journal of Automation and Information Sciences

Editor-in-Chief: Arkadiy A. Chikriy (open in a new tab)

Associate Editors: Vyacheslav F. Gubarev (open in a new tab) Yuriy P. Kondratenko (open in a new tab) Nonna V. Turovyerova (open in a new tab)

Published 12 issues per year

ISSN Print: 1064-2315

ISSN Online: 2163-9337

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Multiobjective Optimization of Evolving Feedforward Neural Networks

Volume 46, Issue 11, 2014, pp. 9-22

DOI: 10.1615/JAutomatInfScien.v46.i11.20

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

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

ABSTRACT

The paper has proposed to utilize a multicriteria approach to training evolutionary feedforward neural networks. The general structure of such neural networks is considered. A comparative analysis of single-objective, scalarized multiobjective learning and Pareto-based multiobjective learning is performed. Simulation in the presence of noisy measurements with different distribution laws has confirmed the effectiveness of the suggested approach.

KEY WORDS: multiobjective optimization, evolutionary feedforward network, multicriteria approach, scalarized multiobjective learning, Pareto-based multiobjective learning

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