Name
Abstract | ||
|---|---|---|
Genetic algorithm (GA) is an efficient method based on the natural selection for global optimization. To take the advantages of GA, the primary goal of this paper is to extend or generalize GA to the m-best subset selection problems. In m-best subset selection, a subset consists of m alternatives is selected from n alternatives to form a group to fulfill a goal most efficiently. This paper concentrates on discussing the possibility of selecting a best subset from n alternatives for certain conditions with constrains. By designing new fitness functions, GA is successfully used in some sorts of certain subset selections. The experimental results show that the improved GA method fulfills the m best subset selection efficiently. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/CCECE.2011.6030526 | CCECE |
Keywords | Field | DocType |
fitness function,m-best problems,genetic algorithm,n alternatives,optimal solutions,global optimization,m-best subset selection,subset selection,fitness functions,genetic algorithms,natural selection,additives,water resources,encoding | Mathematical optimization,Global optimization,Computer science,Natural selection,Fitness function,Artificial intelligence,Genetic algorithm,Encoding (memory) | Conference |
Volume | Issue | ISSN |
null | null | 0840-7789 E-ISBN : 978-1-4244-9787-4 |
ISBN | Citations | PageRank |
978-1-4244-9787-4 | 0 | 0.34 |
References | Authors | |
2 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ping Zhang | 1 | 0 | 0.34 |
| Ju Jiang | 2 | 0 | 1.01 |
| Xueshan Han | 3 | 6 | 4.35 |
| Zhuoxun Lin | 4 | 0 | 0.34 |