Multiobjective optimization methodology : a jumping gene approach

 Content: 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
 Abstract:             \"Complex design problems are often governed by a number of performance merits. These markers gauge how good the design is going to be, but can conflict with the performance requirements that must be met. The challenge is reconciling these two requirements. This book introduces a newly developed jumping gene algorithm, designed to address the multi-functional objectives problem and supplies a viably adequate solution in speed. The text presents various multi-objective optimization techniques and provides the technical know-how for obtaining trade-off solutions between solution spread and convergence\"--
\"Discovered by Nobel Laureate, Barbara McClintock in her work on the corn plants in the nineteen fifties, the phenomenon of Jumping Genes has been traditionally applied in the bio-science and bio-medical fields. Being the first of its kind to introduce the topic of jumping genes outside bio-science/medical areas, this book stands firmly on evolutionary computational ground. Requiring substantial engineering insight and endeavor so that the essence of jumping genes algorithm can be brought out convincingly as well as in scientific style, it has to show its robustness to withstand the unavoidable comparison amongst all the existing algorithms in various theories, practices, and applications. As a new born algorithm, it should undoubtedly carry extra advantages for its uses, where other algorithms could fail or have low capacity\"