Multiobjective optimization methodology : a jumping gene approach
- K.S. Tang ... [et al.].
- Boca Raton : CRC Press, 2012.
- Physical description
- xi, 251 p.,  p. of plates : ill. (some col.) ; 24 cm.
- Industrial electronics series.
Math & Statistics Library
QA402.5 .M8586 2012
- Unknown QA402.5 .M8586 2012
- Tang, K. S., 1967-
- Includes bibliographical references and index.
- Introduction Background on Genetic Algorithms Organization of Chapters References Overview of Multiobjective Optimization Classification of Optimization Methods Multiobjective Algorithms References Jumping Gene Computational Approach Biological Background Overview of Computational Gene Transposition Jumping Gene Genetic Algorithms Real-Coding Jumping Operations Simulation Results References Theoretical Analysis of Jumping Gene Operations Overview of Schema Models Exact Schema Theorem for Jumping Gene Transposition Theorems of Equilibrium and Dynamical Analysis Simulation Results and Analysis Discussion References Performance Measures on Jumping Gene Convergence Metric: Generational Distance Convergence Metric: Deb and Jain Convergence Metric Diversity Metric: Spread Diversity Metric: Extreme Nondominated Solution Generation Binary epsilon-Indicator Statistical Test Using Performance Metrics Jumping Gene Verification and Results References Radio-To-Fiber Repeater Placement in Wireless Local-Loop Systems Introduction Path Loss Model Mathematical Formulation Chromosome Representation Jumping Gene Transposition Chromosome Repairing Results and Discussion References Resource Management in WCDMA Introduction Mathematical Formulation Chromosome Representation Initial Population Jumping Gene Transposition Mutation Ranking Rule Results and Discussion Discussion of Real-Time Implementation References Base Station Placement in WLANs Introduction Path Loss Model Mathematical Formulation Chromosome Representation Jumping Gene Transposition Chromosome Repairing Results and Discussion References Conclusions Reference Appendices Appendix A: Proofs of Lemmas in Chapter 4 Appendix B: Benchmark Test Functions Appendix C: Chromosome Representation Appendix D: Design of the Fuzzy PID Controller.
- (source: Nielsen Book Data)
- Publisher's Summary
- The first book to focus on jumping genes outside bioscience and medicine, Multiobjective Optimization Methodology: A Jumping Gene Approach introduces jumping gene algorithms designed to supply adequate, viable solutions to multiobjective problems quickly and with low computational cost. Better Convergence and a Wider Spread of Nondominated Solutions The book begins with a thorough review of state-of-the-art multiobjective optimization techniques. For readers who may not be familiar with the bioscience behind the jumping gene, it then outlines the basic biological gene transposition process and explains the translation of the copy-and-paste and cut-and-paste operations into a computable language. To justify the scientific standing of the jumping genes algorithms, the book provides rigorous mathematical derivations of the jumping genes operations based on schema theory. It also discusses a number of convergence and diversity performance metrics for measuring the usefulness of the algorithms. Practical Applications of Jumping Gene Algorithms Three practical engineering applications showcase the effectiveness of the jumping gene algorithms in terms of the crucial trade-off between convergence and diversity. The examples deal with the placement of radio-to-fiber repeaters in wireless local-loop systems, the management of resources in WCDMA systems, and the placement of base stations in wireless local-area networks. Offering insight into multiobjective optimization, the authors show how jumping gene algorithms are a useful addition to existing evolutionary algorithms, particularly to obtain quick convergence solutions and solutions to outliers.
(source: Nielsen Book Data)
- Publication date
- Industrial electronics series
- 9781439899199 (hardback)
- 1439899193 (hardback)