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Author: Evgenii Sopov

Affiliation: Siberian State Aerospace University, Russian Federation

ISBN: 978-989-758-201-1

Keyword(s): Large-Scale Global Optimization, Problem Decomposition, Estimation of Distribution Algorithm, Binary Genetic Algorithm.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Computational Intelligence ; Evolutionary Computing ; Genetic Algorithms ; Informatics in Control, Automation and Robotics ; Intelligent Control Systems and Optimization ; Soft Computing

Abstract: In recent years many real-world optimization problems have had to deal with growing dimensionality. Optimization problems with many hundreds or thousands of variables are called large-scale global optimization (LSGO) problems. Many well-known real-world LSGO problems are not separable and are complex for detailed analysis, thus they are viewed as the black-box optimization problems. The most advanced algorithms for LSGO are based on cooperative coevolution schemes using the problem decomposition. These algorithms are mainly proposed for the real-valued search space and cannot be applied for problems with discrete or mixed variables. In this paper a novel technique is proposed, that uses a binary genetic algorithm as the core technique. The estimation of distribution algorithm (EDA) is used for collecting statistical data based on the past search experience to provide the problem decomposition by fixing genes in chromosomes. Such an EDA-based decomposition technique has the benefits of the random grouping methods and the dynamic learning methods. The EDA-based decomposition GA using the island model is also discussed. The results of numerical experiments for benchmark problems from the CEC competition are presented and discussed. The experiments show that the approach demonstrates efficiency comparable to other advanced techniques. (More)

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Paper citation in several formats:
Sopov, E. (2016). EDA-based Decomposition Approach for Binary LSGO Problems.In Proceedings of the 8th International Joint Conference on Computational Intelligence - Volume 3: ECTA, (IJCCI 2016) ISBN 978-989-758-201-1, pages 132-139. DOI: 10.5220/0006034301320139

@conference{ecta16,
author={Evgenii Sopov.},
title={EDA-based Decomposition Approach for Binary LSGO Problems},
booktitle={Proceedings of the 8th International Joint Conference on Computational Intelligence - Volume 3: ECTA, (IJCCI 2016)},
year={2016},
pages={132-139},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006034301320139},
isbn={978-989-758-201-1},
}

TY - CONF

JO - Proceedings of the 8th International Joint Conference on Computational Intelligence - Volume 3: ECTA, (IJCCI 2016)
TI - EDA-based Decomposition Approach for Binary LSGO Problems
SN - 978-989-758-201-1
AU - Sopov, E.
PY - 2016
SP - 132
EP - 139
DO - 10.5220/0006034301320139

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