A multi-objective genetic algorithm based on quick sort
文献类型：期刊论文
| 作者 | Zheng, JH; Ling, C; Shi, ZZ; Xue, J; Li, XY |
| 刊名 | ADVANCES IN ARTIFICIAL INTELLIGENCE
 |
| 出版日期 | 2004 |
| 卷号 | 3060页码:175-186 |
| ISSN号 | 0302-9743 |
| 英文摘要 | The Multi-objective Genetic Algorithms (MOGA) based on Pareto Optimum have been widely applied to solve multi-objective optimal problems, mainly because of their ability to find a set of candidate solutions within a single run. In MOGAs, a non-dominated set is a set of candidate solutions, so it is very important to construct the non-dominated set efficiently. In this paper, the relation of individuals and their related features are discussed. It is proved that the individuals of an evolutionary population can be sorted by quick sort. We construct the non-dominated set of the evolutionary population with quick sort, and the time complexity of the construction is O(nlogn), compared to the previous best result of O(n(2)) described in the popular NSGA-II [Deb, 2002]. We further propose a multi-objective genetic algorithm based on quick sort, and two benchmark problems are experimented. We show that the results of the experiments match to our theoretical analysis. |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000221818900013 |
| 出版者 | SPRINGER-VERLAG BERLIN |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/13865]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Zheng, JH |
| 作者单位 | 1.Xiangtan Univ, Coll Informat Engn, Hunan, Peoples R China 2.Univ Western Ontario, Dept Comp Sci, London, ON N6A 5B7, Canada 3.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zheng, JH,Ling, C,Shi, ZZ,et al. A multi-objective genetic algorithm based on quick sort[J]. ADVANCES IN ARTIFICIAL INTELLIGENCE,2004,3060:175-186. |
| APA | Zheng, JH,Ling, C,Shi, ZZ,Xue, J,&Li, XY.(2004).A multi-objective genetic algorithm based on quick sort.ADVANCES IN ARTIFICIAL INTELLIGENCE,3060,175-186. |
| MLA | Zheng, JH,et al."A multi-objective genetic algorithm based on quick sort".ADVANCES IN ARTIFICIAL INTELLIGENCE 3060(2004):175-186. |
入库方式： OAI收割
来源：计算技术研究所
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