A Grid-based Genetic Algorithm for Multimodal Real Function Optimization

Jose M. Chaquet, Enrique J. Carmona

Abstract

A novel genetic algorithm called GGA (Grid-based Genetic Algorithm) is presented to improve the optimization of multimodal real functions. The search space is discretized using a grid, making the search process more efficient and faster. An integer-real vector codes the genotype and a GA is used for evolving the population. The integer part allows us to explore the search space and the real part to exploit the best solutions. A comparison with a standard GA is performed using typical benchmarking multimodal functions from the literature. In all the tested problems, the proposed algorithm equals or outperforms the standard GA.

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Paper Citation


in Harvard Style

M. Chaquet J. and J. Carmona E. (2012). A Grid-based Genetic Algorithm for Multimodal Real Function Optimization . In Proceedings of the 4th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2012) ISBN 978-989-8565-33-4, pages 158-163. DOI: 10.5220/0004114401580163


in Bibtex Style

@conference{ecta12,
author={Jose M. Chaquet and Enrique J. Carmona},
title={A Grid-based Genetic Algorithm for Multimodal Real Function Optimization},
booktitle={Proceedings of the 4th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2012)},
year={2012},
pages={158-163},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004114401580163},
isbn={978-989-8565-33-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 4th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2012)
TI - A Grid-based Genetic Algorithm for Multimodal Real Function Optimization
SN - 978-989-8565-33-4
AU - M. Chaquet J.
AU - J. Carmona E.
PY - 2012
SP - 158
EP - 163
DO - 10.5220/0004114401580163