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Content Provider | IEEE Xplore Digital Library |
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Author | Guoli Zhang Mingxin Dou Siyan Wang |
Copyright Year | 2009 |
Abstract | This paper proposes a new hybrid genetic algorithm which combine with the particle swarm optimization technique in order to improve the search efficiency of classical genetic algorithm. This algorithm gives a new crossover operation and a mutation strategy based on the idea of particle swarm optimization. The experiment results show that the new algorithm can obtain better results than competitive algorithm in the average convergence generation and the global convergence probability. |
Starting Page | 103 |
Ending Page | 106 |
File Size | 298221 |
Page Count | 4 |
File Format | |
ISBN | 9781424454112 |
e-ISBN | 9781424454129 |
DOI | 10.1109/CIS.2009.236 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2009-12-11 |
Publisher Place | China |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | particle swarm crossover operation Genetic mutations Optimization methods Educational institutions Mathematics function optimization Particle swarm optimization Physics Genetic algorithms Convergence genetic algorithm Computer security mutation operation Computational intelligence |
Content Type | Text |
Resource Type | Article |
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