Several Populations Genetic Algorithms

DOI

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.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Chunyan Liao
PY  - 2013/08
DA  - 2013/08
TI  - Several Populations Genetic Algorithms
BT  - Proceedings of the Third International Conference on Control, Automation and Systems Engineering (CASE-13)
PB  - Atlantis Press
SP  - 68
EP  - 71
SN  - 1951-6851
UR  - https://doi.org/10.2991/case-13.2013.17
DO  - 10.2991/case-13.2013.17
ID  - Liao2013/08
ER  -