Advances in Evolutionary Computing for System Design (Studies in Computational Intelligence) (Studies in Computational Intelligence)

Evolutionary computing paradigms offer robust and powerful adaptive search mechanisms for system design. This book includes thirteen chapters covering a wide area of topics in evolutionary computing and applications including: + Introduction to evolutionary computing in system design + Evolutionary neuro-fuzzy systems + Evolution of fuzzy controllers + Genetic algorithms for multi-classifier design + Evolutionary grooming of traffic + Evolutionary particle swarms + Fuzzy logic systems using genetic algorithms + Evolutionary algorithms and immune learning for neural network-based controller design + Distributed problem solving using evolutionary learning + Evolutionary computing within grid environment + Evolutionary game theory in wireless mesh networks + Hybrid multiobjective evolutionary algorithms for the sailor assignment problem + Evolutionary techniques in hardware optimization This book will be useful to researchers in intelligent systems with interest in evolutionary computing, application engineers and system designers. The book can also be used by students and lecturers as an advanced reading material for courses on evolutionary computing.

Evolutionary computing paradigms offer robust and powerful adaptive search mechanisms for system design. This book includes thirteen chapters covering a wide area of topics in evolutionary computing and applications including:

• Introduction to evolutionary computing in system design


• Evolutionary neuro-fuzzy systems


• Evolution of fuzzy controllers


• Genetic algorithms for multi-classifier design


• Evolutionary grooming of traffic


• Evolutionary particle swarms


• Fuzzy logic systems using genetic algorithms


• Evolutionary algorithms and immune learning for neural network-based controller design


• Distributed problem solving using evolutionary learning


• Evolutionary computing within grid environment


• Evolutionary game theory in wireless mesh networks


• Hybrid multiobjective evolutionary algorithms for the sailor assignment problem


• Evolutionary techniques in hardware optimization

This book will be useful to researchers in intelligent systems with interest in evolutionary computing, application engineers and system designers. The book can also be used by students and lecturers as an advanced reading material for courses on evolutionary computing.

Front Matter....Pages I-VIII Introduction to Evolutionary Computing in System Design....Pages 1-9 Evolutionary Neuro-Fuzzy Systems and Applications....Pages 11-45 Evolution of Fuzzy Controllers and Applications....Pages 47-69 A Neuro-Genetic Framework for Multi-Classifier Design: An Application to Promoter Recognition in DNA Sequences....Pages 71-94 Evolutionary Grooming of Traffic in WDM Optical Networks....Pages 95-137 EPSO: Evolutionary Particle Swarms....Pages 139-167 Design of Type-Reduction Strategies for Type-2 Fuzzy Logic Systems using Genetic Algorithms....Pages 169-187 Designing a Recurrent Neural Network-based Controller for Gyro-Mirror Line-of-Sight Stabilization System using an Artificial Immune Algorithm....Pages 189-209 Distributed Problem Solving using Evolutionary Learning in Multi-Agent Systems....Pages 211-227 Evolutionary Computing within Grid Environment....Pages 229-248 Application of Evolutionary Game Theory to Wireless Mesh Networks....Pages 249-267 Applying Hybrid Multiobjective Evolutionary Algorithms to the Sailor Assignment Problem....Pages 269-301 Evolutionary Techniques Applied to Hardware Optimization Problems: Test and Verification of Advanced Processors....Pages 303-326 Evolutionary computing paradigms offer robust and powerful adaptive search mechanisms for system design. This books thirteen chapters cover a wide area of topics in evolutionary computing and applications, including an introduction to evolutionary computing in system design; evolutionary neuro-fuzzy systems; and evolution of fuzzy controllers. The book will be useful to researchers in intelligent systems with interest in evolutionary computing, as well as application engineers and system designers.