Grouping genetic algorithm for the blockmodel problem

Authors:
Tabitha James;Evelyn Brown;Cliff T. Ragsdale
Affiliations:
Department of Business Information Technology, Pamplin College of Business, Virginia Polytechnic Institute and State University, Blacksburg, VA;Department of Engineering, College of Technology and Computer Science, East Carolina University, Greenville, NC;Department of Business Information Technology, Pamplin College of Business, Virginia Polytechnic Institute and State University, Blacksburg, VA
Venue:
IEEE Transactions on Evolutionary Computation
Year:
2010

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Abstract

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.