Proceedings of the Third International Conference on Control, Automation and Systems Engineering (CASE-13)

Several Populations Genetic Algorithms

Authors
Chunyan Liao
Corresponding Author
Chunyan Liao
Available Online August 2013.
DOI
https://doi.org/10.2991/case-13.2013.17How to use a DOI?
Keywords
Several populations; genetic algorithm; improved algorithm; prematurity
Abstract
In the traditional genetic algorithm that the convergence of the existence of slow convergence and local convergence problems, the introduction of a variety of groups based on the standard genetic algorithm to overcome the premature convergence of genetic algorithms. After taking into account the evolution of genetic fitness of the individual problems in the algorithm used in populations of competitive methods, in specific populations to adapt to constantly out of the individual is low, and continue to add new individuals to increase the diversity of the population in order to improve convergence speed. Test function of a typical experiment, the results with other multi-group comparison of the results of genetic algorithm, the results demonstrate that the new algorithm is superior in avoiding premature convergence to find high-quality solutions.
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Proceedings
Third International Conference on Control, Automation and Systems Engineering (CASE-13)
Part of series
Advances in Intelligent Systems Research
Publication Date
August 2013
ISBN
978-90786-77-81-9
ISSN
1951-6851
DOI
https://doi.org/10.2991/case-13.2013.17How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Chunyan Liao
PY  - 2013/08
DA  - 2013/08
TI  - Several Populations Genetic Algorithms
BT  - Third International Conference on Control, Automation and Systems Engineering (CASE-13)
PB  - Atlantis Press
SN  - 1951-6851
UR  - https://doi.org/10.2991/case-13.2013.17
DO  - https://doi.org/10.2991/case-13.2013.17
ID  - Liao2013/08
ER  -