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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Zhou Weihong Xiong Shunqing Liu Ying |
| Copyright Year | 2010 |
| Description | Author affiliation: National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100012, China (Zhou Weihong) || School of Mathematics & Computer Science, Yunnan Nationalities University, 650031, China (Xiong Shunqing; Liu Ying) |
| Abstract | It is difficult for basic genetic algorithm and evolutionary programming algorithm to converge at global optimal solution of real-continual function in practice, although both of the algorithms have the ability for getting the global optimal solution with convergent probabilities 1 in theory. In this paper, a new hybrid genetic algorithm based on clan competition is proposed, and it is proved that the probability of the new algorithm convegent to the global optimal solution is 1, Numerical experiments results illustrate that, compared with the former two algorithms, the new algorithm is the robustest among the three algorithms, what's more, it has the highest precision with the equal parameters. |
| Starting Page | 65 |
| Ending Page | 68 |
| File Size | 372336 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424488834 |
| e-ISBN | 9781424488865 |
| DOI | 10.1109/YCICT.2010.5713153 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-11-28 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Algorithm design and analysis Hybrid genetic algorithm Evolution (biology) Basic genetic algorithm Evolutionary programming algorithm Markov Chain Programming Markov processes Clan competition Optimization Convergence Genetic algorithms |
| Content Type | Text |
| Resource Type | Article |
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