TWO-POPULATION GENETIC ALGORITHM - An Approach to Improve the Population Diversity

M. Gestal, D. Rivero, E. Fernández, J. R. Rabuñal, J. Dorado

Abstract

Genetic Algorithms (GAs) are a technique that has given good results to those problems that require a search through a complex space of possible solutions. A key point of GAs is the necessity of maintaining the diversity in the population. Without this diversity, the population converges and the search prematurely stops, not being able to reach the optimal solution. This is a very common situation in GAs. This paper proposes a modification in traditional GAs to overcome this problem, avoiding the loose of diversity in the population. This modification allows an exhaustive search that will provide more than one valid solution in the same execution of the algorithm.

References

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Paper Citation


in Harvard Style

Gestal M., Rivero D., Fernández E., Rabuñal J. and Dorado J. (2010). TWO-POPULATION GENETIC ALGORITHM - An Approach to Improve the Population Diversity . In Proceedings of the 2nd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-674-021-4, pages 635-639. DOI: 10.5220/0002760306350639


in Bibtex Style

@conference{icaart10,
author={M. Gestal and D. Rivero and E. Fernández and J. R. Rabuñal and J. Dorado},
title={TWO-POPULATION GENETIC ALGORITHM - An Approach to Improve the Population Diversity},
booktitle={Proceedings of the 2nd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2010},
pages={635-639},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002760306350639},
isbn={978-989-674-021-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 2nd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - TWO-POPULATION GENETIC ALGORITHM - An Approach to Improve the Population Diversity
SN - 978-989-674-021-4
AU - Gestal M.
AU - Rivero D.
AU - Fernández E.
AU - Rabuñal J.
AU - Dorado J.
PY - 2010
SP - 635
EP - 639
DO - 10.5220/0002760306350639