Biologically-Inspired Optimisation Methods

Parallel Algorithms, Systems and Applications

  • Andrew Lewis
  • Sanaz Mostaghim
  • Marcus Randall
Part of the Studies in Computational Intelligence book series (SCI, volume 210)

Table of contents

  1. Front Matter
  2. Antonio López Jaimes, Carlos A. Coello Coello
    Pages 23-49
  3. Andrew Lewis, Sanaz Mostaghim, Ian Scriven
    Pages 51-78
  4. Tim Hendtlass, Irene Moser, Marcus Randall
    Pages 79-109
  5. Marcus Randall, Tim Hendtlass, Andrew Lewis
    Pages 139-164
  6. Daniel Angus
    Pages 165-188
  7. Andrew Lewis, Marcus Randall, Amir Galehdar, David Thiel, Gerhard Weis
    Pages 189-217
  8. Sílvio P. Mendes, Juan A. Gómez-Pulido, Miguel A. Vega-Rodríguez, Juan M. Sánchez-Pérez, Yago Sáez, Pedro Isasi
    Pages 219-260
  9. Andreas Kamper, Anke Eßer
    Pages 261-289
  10. Alexandru-Adrian Tantar, Nouredine Melab, El-Ghazali Talbi
    Pages 291-323
  11. H. Y. Quek, H. H. Chan, K. C. Tan, A. Tay
    Pages 325-354
  12. Back Matter

About this book

Introduction

Humanity has often turned to Nature for inspiration to help it solve its problems.  The systems She provides are often based on simple rules and premises, yet are able to adapt to new and complex environments quickly and efficiently.  Problems from a range of human endeavours, including, science, engineering and economics, require us to find good quality solutions in exponentially large search spaces, a task that often requires vast amounts computational resources and effort.  In this book, the contributing authors solve these problems by modelling aspects of the natural world, from the flocking of birds and fish, the operation of colonies of ants through to chromosome reproduction and beyond.  Many of the contributions represent extended studies of work presented at a number of workshops on Biologically-Inspired Optimisation Methods at international conferences on e-Science, Grid Computing, and Evolutionary Computation.  A variety of chapters from some of the leading experts in the field present an overview of the state-of-the-art, recent advances in theoretical and practical ideas and techniques, and details of application of these methods to a range of benchmark and real world problems.

Keywords

Extension algorithm algorithms computational intelligence evolution evolutionary algorithm genetic algorithms grid computing heuristics learning metaheuristic neural networks optimization problem solving radio-frequency identification (RFID)

Editors and affiliations

  • Andrew Lewis
    • 1
  • Sanaz Mostaghim
    • 2
  • Marcus Randall
    • 3
  1. 1.Institute for Integrated and Intelligent SystemsGriffith UniversityBrisbaneAustralia
  2. 2.Institut für Angewandte Informatik und Formale Beschreibungsverfahren - AIFBUniversität KarlsruheKarlsruheGermany
  3. 3.Faculty of Business, Technology and Sustainable DevelopmentBond UniversityGold CoastAustralia

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-01262-4
  • Copyright Information Springer Berlin Heidelberg 2009
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-642-01261-7
  • Online ISBN 978-3-642-01262-4
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503