Evolution in Groups: A Genetic Algorithm Approach to Group Decision Support Systems

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Author

J Rees, G Koehler

Tech report number

CERIAS TR 2001-123

Entry type

article

Abstract

Certain tasks undertaken by groups using Group Decision Support Systems (GDSS) can be viewed as search problems. These tasks involve arriving at a solution or decision where the problem is complex enough to warrant the use of computerized decision support tools. For these types of GDSS tasks, we propose to model the information exchange and convergence toward a solution by the group as a simple genetic algorithm. The simple genetic algorithm is a generalized search technique that is based on the principles of evolution and natural selection. Simply put, the best points in the current population are more likely to be selected and combined through genetic operators to determine new points. We propose that groups using GDSS to address certain tasks behave like a simple genetic algorithm in the manner in which possible solutions are generated, enhanced and altered in attempting to reach a decision or consensus.

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Date

2002

URL

http://www.springerlink.com/co ... e7349r0hxqcd/fulltext.pdf

Address

The Netherlands

Journal

Information Technology and Management

Key alpha

Rees

Number

Pages

213-227

Publisher

Kluwer Academic Publishers

Volume

Publication Date

2002-00-00

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@Article{ Rees,
	title = "Evolution in Groups: A Genetic Algorithm Approach to Group Decision Support Systems",
	author = "J Rees, G Koehler",
	year = "2002",
	address = "The Netherlands",
	journal = "Information Technology and Management",
	number = "3",
	pages = "213-227",
	publisher = "Kluwer Academic Publishers",
	volume = "3",
	abstract = "Certain tasks undertaken by groups using Group Decision Support Systems (GDSS) can be viewed as search problems. These tasks involve arriving at a solution or decision where the problem is complex enough to warrant the use of computerized decision support tools. For these types of GDSS tasks, we propose to model the information exchange and convergence toward a solution by the group as a simple genetic algorithm. The simple genetic algorithm is a generalized search technique that is based on the
principles of evolution and natural selection. Simply put, the best points in the current population are more
likely to be selected and combined through genetic operators to determine new points. We propose that
groups using GDSS to address certain tasks behave like a simple genetic algorithm in the manner in which
possible solutions are generated, enhanced and altered in attempting to reach a decision or consensus.",
	url = "http://www.springerlink.com/content/6jtfe7349r0hxqcd/fulltext.pdf",
}