Multi-Objective Optimization in Computational Intelligence: Theory and Practice

Bui, Lam Thu

ISBN 10: 1599044986 / ISBN 13: 9781599044989

Published by Information Science Reference, 2008

About this Item

Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Multi-objective optimization (MO) is part of computational intelligence research. This work explores the theoretical, as well as performance of MOs on a range of optimization issues including combinatorial, real-valued, dynamic, and noisy problems. It also .

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Bibliographic Details

• Title: Multi-Objective Optimization in Computational Intelligence: Theory and Practice
• Publisher: Information Science Reference
• Publication Date: 2008
• Language: English
• ISBN 10: 1599044986
• ISBN 13: 9781599044989
• Binding: Gebunden
• Condition: New

About this title

Synopsis

Multi-objective optimization (MO) is a fast-developing field in computational intelligence research. Giving decision makers more options to choose from using some post-analysis preference information, there are a number of competitive MO techniques with an increasingly large number of MO real-world applications. Multi-Objective Optimization in Computational Intelligence: Theory and Practice explores the theoretical, as well as empirical, performance of MOs on a wide range of optimization issues including combinatorial, real-valued, dynamic, and noisy problems. This book provides scholars, academics, and practitioners with a fundamental, comprehensive collection of research on multi-objective optimization techniques, applications, and practices.

About the Author

Lam Thu Bui is a Research Fellow at the School of ITEE, University of New South Wales at Australian Defence Force Academy. He is currently doing research in the field of evolutionary computation, specialized with Evolutionary Multi-Objective Optimization. He holds a Bachelor of Informatics, a Masters Degree in Information Technology, and a PhD in Computer. He has been involved with academic area including teaching and researching for over seven years and about 20 refereed journal and conference papers and book chapters related to multi-objective optimization. He has been a member of the program committees of several conferences and workshops in the field of evolutionary computing, such as the IEEE Congress on Evolutionary Computation (CEC) and The Genetic and Evolutionary Computation Conference (GECCO).

Sameer Alam is a Research Fellow at the Defence and Security Applications Research Centre, University of New South Wales at the Australian Defence Force Academy, Canberra, Australia. He holds B.S. in Maths, M.A. in Economics and M.Tech. in Comp. Sc. He recently submitted his Ph.D. to the University of New South Wales. He has published over 9 refereed journal and conference papers mainly in the area of air traffic management. His research interest includes multi-objective optimization, heuristic search and swarm intelligence. He is invited reviewer for a number of journal and conferences including IEEE Transactions on Intelligent Transportation Systems, Journal of Transportation Science-C, Elsevier Science, 2nd International Conference on Bio-Inspired Computing in China, 2007, 6th International Conference on Simulated Evolution and Learning in China, 2006 and 2nd IFIP Conference on Biologically Inspired Collaborative Computing in Italy, 2008. He was also the program committee member for the first IEEE Symposium on Artificial Life 2007 in Honolulu, Hawaii, USA.