Multi-Strategy Genetic Algorithm for Multimodal Optimization

Evgenii Sopov

2015

Abstract

Multimodal optimization (MMO) is the problem of finding many or all global and local optima. In recent years many efficient nature-inspired techniques (based on ES, PSO, DE and others) have been proposed for real-valued problems. Many real-world problems contain variables of many different types, including integer, rank, binary and others. In this case, the weakest representation (namely binary representation) is used. Unfortunately, there is a lack of efficient approaches for problems with binary representation. Existing techniques are usually based on general ideas of niching. Moreover, there exists the problem of choosing a suitable algorithm and fine tuning it for a certain problem. In this study, a novel approach based on a metaheuristic for designing multi-strategy genetic algorithm is proposed. The approach controls the interactions of many search techniques (different genetic algorithms for MMO) and leads to the self-configuring solving of problems with a priori unknown structure. The results of numerical experiments for classical benchmark problems and benchmark problems from the CEC competition on MMO are presented. The proposed approach has demonstrated efficiency better than standard niching techniques and comparable to advanced algorithms. The main feature of the approach is that it does not require the participation of the human-expert, because it operates in an automated, self-configuring way.

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Paper Citation


in Harvard Style

Sopov E. (2015). Multi-Strategy Genetic Algorithm for Multimodal Optimization . In Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: ECTA, ISBN 978-989-758-157-1, pages 55-63. DOI: 10.5220/0005592000550063


in Bibtex Style

@conference{ecta15,
author={Evgenii Sopov},
title={Multi-Strategy Genetic Algorithm for Multimodal Optimization},
booktitle={Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: ECTA,},
year={2015},
pages={55-63},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005592000550063},
isbn={978-989-758-157-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: ECTA,
TI - Multi-Strategy Genetic Algorithm for Multimodal Optimization
SN - 978-989-758-157-1
AU - Sopov E.
PY - 2015
SP - 55
EP - 63
DO - 10.5220/0005592000550063