Grouping Genetic Algorithm for the Blockmodel Problem

被引：11
|
作者：
James, Tabitha [1 ]
Brown, Evelyn [2 ]
Ragsdale, Cliff T. [1 ]
机构：
[1] Virginia Polytech Inst & State Univ, Dept Business Informat Technol, RB Pamplin Coll Business, Blacksburg, VA 24061 USA
[2] E Carolina Univ, Dept Engn, Coll Technol & Comp Sci, Greenville, NC 27858 USA
来源：
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION | 2010年 / 14卷 / 01期
关键词：
Blockmodel; grouping genetic algorithm (GGA); social network analysis;
D O I：
10.1109/TEVC.2009.2023793
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Many areas of research examine the relationships between objects. A subset of these research areas focuses on methods for creating groups whose members are similar based on some specific attribute(s). The blockmodel problem has as its objective to group objects in order to obtain a small number of large groups of similar nodes. In this paper, a grouping genetic algorithm (GGA) is applied to the blockmodel problem. Testing on numerous examples from the literature indicates a GGA is an appropriate tool for solving this type of problem. Specifically, our GGA provides good solutions, even to large-size problems, in reasonable computational time.
引用
收藏
页码：103 / 111
页数：9
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