Introduction. Overview of Multiobjective Optimization. Jumping Gene Computational Approach. Theoretical Analysis of JG. Performance Measures on JG. Radio-To-Fiber Repeater Placement in Wireless Local Loop Systems. Resource Management in WCDMA. Base Station Placement in WLANs. Conclusions.
Kit Sang Tang received his BSc from the University of Hong Kong in 1988 and his MSc and PhD from City University of Hong Kong in 1992 and 1996, respectively. He is currently an associate professor in the Department of Electronic Engineering at City University of Hong Kong. He has published over 90 journal papers and five book chapters, and coauthored two books, focusing on genetic algorithms and chaotic theory.
Tak Ming Chan received his BSc in applied physics from Hong Kong Baptist University in 1999 and his MPhil and PhD in electronic engineering from City University of Hong Kong in 2001 and 2006 respectively. He was a research associate in the Department of Industrial and Systems Engineering at the Hong Kong Polytechnic University from 2006 to 2007 and a postdoctoral fellow in the Department of Production and Systems Engineering, University of Minho, Portugal from 2007 to 2009.
Richard Jacob Yin obtained his BEng in Information Technology in 2004 and his PhD in Electronic Engineering in 2010 from the City University of Hong Kong. He is now an Electronic Engineer at ASM Assembly Automation Hong Kong Limited.
Kim Fung Man is a Chair Professor and head of the electronic engineering department at City University of Hong Kong. He received his PhD from Cranfield Institute of Technology, UK. He is currently the co-editor-in-chief of IEEE Transactions of Industrial Electronics. He has co-authored three books and published extensively in the area.
"This is an interesting and practical book. It is easy to read
[and] provides good background information ... [and] cutting-edge
technologies to solve the challenging multi-objective optimization
problems."
—Mo-Yuen Chow, North Carolina State University, Raleigh, USA"The
authors describe the jumping gene approach to solve multiobjective
optimization problems. It is quite [a] new approach and complements
standard operations used in genetic algorithms."
—Marcin Anholcer (Poznan), Zentralblatt MATH 1273
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