IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Evolutionary Multi-Objective Optimization for Assignment Problems

Evolutionary Multi-Objective Optimization for Assignment Problems
View Sample PDF
Author(s): Mark P. Kleeman (Air Force Institute of Technology, USA) and Gary B. Lamont (Air Force Institute of Technology, USA)
Copyright: 2008
Pages: 24
Source title: Multi-Objective Optimization in Computational Intelligence: Theory and Practice
Source Author(s)/Editor(s): Lam Thu Bui (University of New South Wales, Australia) and Sameer Alam (University of New South Wales, Australia)
DOI: 10.4018/978-1-59904-498-9.ch013

Purchase

View Evolutionary Multi-Objective Optimization for Assignment Problems on the publisher's website for pricing and purchasing information.

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.

Related Content

Paolo Massimo Buscema, William J. Tastle. © 2020. 29 pages.
Uthra Kunathur Thikshaja, Anand Paul. © 2020. 11 pages.
Arvind Kumar Tiwari. © 2020. 11 pages.
Srijan Das, Arpita Dutta, Saurav Sharma, Sangharatna Godboley. © 2020. 17 pages.
Mohammed E. El-Telbany, Samah Refat, Engy I. Nasr. © 2020. 13 pages.
Ashraf M. Abdelbar, Islam Elnabarawy, Donald C. Wunsch II, Khalid M. Salama. © 2020. 14 pages.
Saifullah Khalid. © 2020. 12 pages.
Body Bottom