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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Guo-Sheng Hao Chang-Shuai Chen Gai-Ge Wang Ping Ling Ya-Li Liu Zhao-Jun Zhang De-Xuan Zou Yong-Qing Huang |
| Copyright Year | 2015 |
| Description | Author affiliation: Sch. of Comput. Sci. & Technol., Jiangsu Normal Univ., Xuzhou, China (Guo-Sheng Hao; Chang-Shuai Chen; Gai-Ge Wang; Ping Ling; Ya-Li Liu) || Sch. of Electr. Eng. & Autom., Jiangsu Normal Univ., Xuzhou, China (Zhao-Jun Zhang; De-Xuan Zou; Yong-Qing Huang) |
| Abstract | There are two kinds of methods to determine parameters in GAs: online and offline. This paper studied the offline determinations of parameters from the decision space but not fitness landscape. In order to make full use of operators' ability to explore/exploit the subspace, the population size and terminal generation number should satisfy two conditions: (1) for each individual in the search space, the probability to be visited is greater than 0; (2) the total number of solutions that the algorithm visits should be no more than the search space size. Based on these two conditions, the upper bound of terminal generation number and the lower bound of mutation probability were given. And from the viewpoints of the subspace that crossover and mutation can cover, the value determinations for these low bound of population size and the low bound of termination generation number were proposed. The results proposed in this paper provide the theoretic basis for the application of GAs. |
| Starting Page | 239 |
| Ending Page | 244 |
| File Size | 756764 |
| Page Count | 6 |
| File Format | |
| ISSN | 21579563 |
| e-ISBN | 9781467376792 |
| DOI | 10.1109/ICNC.2015.7377997 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2015-08-15 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | crossover Estimation Statistics Optimization population size Genetic algorithms genetic algorithm searching ability mutation Upper bound generation number Sociology Space exploration |
| Content Type | Text |
| Resource Type | Article |
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