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Evolutionary Multi-Objective Optimization for Assignment Problems

Evolutionary Multi-Objective Optimization for Assignment Problems

Mark P. Kleeman, Gary B. Lamont
Copyright: © 2008 |Pages: 24
ISBN13: 9781599044989|ISBN10: 1599044986|ISBN13 Softcover: 9781616926878|EISBN13: 9781599045009
DOI: 10.4018/978-1-59904-498-9.ch013
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MLA

Kleeman, Mark P., and Gary B. Lamont. "Evolutionary Multi-Objective Optimization for Assignment Problems." Multi-Objective Optimization in Computational Intelligence: Theory and Practice, edited by Lam Thu Bui and Sameer Alam, IGI Global, 2008, pp. 364-387. https://doi.org/10.4018/978-1-59904-498-9.ch013

APA

Kleeman, M. P. & Lamont, G. B. (2008). Evolutionary Multi-Objective Optimization for Assignment Problems. In L. Thu Bui & S. Alam (Eds.), Multi-Objective Optimization in Computational Intelligence: Theory and Practice (pp. 364-387). IGI Global. https://doi.org/10.4018/978-1-59904-498-9.ch013

Chicago

Kleeman, Mark P., and Gary B. Lamont. "Evolutionary Multi-Objective Optimization for Assignment Problems." In Multi-Objective Optimization in Computational Intelligence: Theory and Practice, edited by Lam Thu Bui and Sameer Alam, 364-387. Hershey, PA: IGI Global, 2008. https://doi.org/10.4018/978-1-59904-498-9.ch013

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Abstract

Assignment problems are used throughout many research disciplines. Most assignment problems in the literature have focused on solving a single objective. This chapter focuses on assignment problems that have multiple objectives that need to be satisfied. In particular, this chapter looks at how multi-objective evolutionary algorithms have been used to solve some of these problems. Additionally, this chapter examines many of the operators that have been utilized to solve assignment problems and discusses some of the advantages and disadvantages of using specific operators.

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