A Grid-based Genetic Algorithm for Multimodal Real Function Optimization

Jose M. Chaquet, Enrique J. Carmona

2012

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.

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