Advances in Evolutionary Computing for System Design

  • Lakhmi C. Jain
  • Vasile Palade
  • Dipti Srinivasan

Part of the Studies in Computational Intelligence book series (SCI, volume 66)

Table of contents

  1. Front Matter
    Pages I-VIII
  2. Lakhmi C. Jain, Shing Chiang Tan, Chee Peng Lim
    Pages 1-9
  3. G. Castellano, C. Castiello, A. M. Fanelli, L. Jain
    Pages 11-45
  4. Dilip Kumar Pratihar, Nirmal Baran Hui
    Pages 47-69
  5. V. Miranda, Hrvoje Keko, Alvaro Jaramillo
    Pages 139-167
  6. Ashutosh Tiwari, Gokop Goteng, Rajkumar Roy
    Pages 229-248
  7. Athanasios Vasilakos, Markos Anastasopoulos
    Pages 249-267
  8. Deon Garrett, Dipankar Dasgupta, Joseph Vannucci, James Simien
    Pages 269-301

About this book

Introduction

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.

Keywords

Multi-agent system algorithm algorithms cognition evolution evolutionary algorithm evolutionary computation game theory genetic algorithm intelligent systems learning neural network optimization problem solving verification

Editors and affiliations

  • Lakhmi C. Jain
    • 1
  • Vasile Palade
    • 2
  • Dipti Srinivasan
    • 3
  1. 1.University of South AustraliaSouth AustraliaAustralia
  2. 2.Computing LaboratoryOxfordEngland
  3. 3.National University of SingaporeSingapore

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-540-72377-6
  • Copyright Information Springer-Verlag Berlin Heidelberg 2007
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-540-72376-9
  • Online ISBN 978-3-540-72377-6
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • About this book
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