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Biologically-Inspired Optimisation Methods

Parallel Algorithms, Systems and Applications

  • Book
  • © 2009

Overview

Editors:
  1. Andrew Lewis

    1. Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, Australia

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  2. Sanaz Mostaghim

    1. Institut für Angewandte Informatik und Formale Beschreibungsverfahren - AIFB, Universität Karlsruhe, Karlsruhe, Germany

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    You can also search for this editor in PubMed   Google Scholar

  3. Marcus Randall

    1. Faculty of Business, Technology and Sustainable Development, Bond University, Gold Coast, Australia

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  • Presents recent research in Biologically-inspired Optimisation Methods

Part of the book series: Studies in Computational Intelligence (SCI, volume 210)

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Table of contents (12 chapters)

  1. Front Matter

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  2. Evolution’s Niche in Multi-Criterion Problem Solving

    • Kalyanmoy Deb
    Pages 1-21

  3. Applications of Parallel Platforms and Models in Evolutionary Multi-Objective Optimization

    • Antonio López Jaimes, Carlos A. Coello Coello
    Pages 23-49

  4. Asynchronous Multi-Objective Optimisation in Unreliable Distributed Environments

    • Andrew Lewis, Sanaz Mostaghim, Ian Scriven
    Pages 51-78

  5. Dynamic Problems and Nature Inspired Meta-heuristics

    • Tim Hendtlass, Irene Moser, Marcus Randall
    Pages 79-109

  6. Relaxation Labelling Using Distributed Neural Networks

    • Jim Austin
    Pages 111-138

  7. Extremal Optimisation for Assignment Type Problems

    • Marcus Randall, Tim Hendtlass, Andrew Lewis
    Pages 139-164

  8. Niching for Ant Colony Optimisation

    • Daniel Angus
    Pages 165-188

  9. Using Ant Colony Optimisation to Construct Meander-Line RFID Antennas

    • Andrew Lewis, Marcus Randall, Amir Galehdar, David Thiel, Gerhard Weis
    Pages 189-217

  10. The Radio Network Design Optimization Problem

    • Sílvio P. Mendes, Juan A. Gómez-Pulido, Miguel A. Vega-Rodríguez, Juan M. Sánchez-Pérez, Yago Sáez, Pedro Isasi
    Pages 219-260

  11. Strategies for Decentralised Balancing Power

    • Andreas Kamper, Anke Eßer
    Pages 261-289

  12. An Analysis of Dynamic Mutation Operators for Conformational Sampling

    • Alexandru-Adrian Tantar, Nouredine Melab, El-Ghazali Talbi
    Pages 291-323

  13. Evolving Computer Chinese Chess Using Guided Learning

    • H. Y. Quek, H. H. Chan, K. C. Tan, A. Tay
    Pages 325-354

  14. Back Matter

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Keywords

About this book

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.

Editors and Affiliations

  • Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, Australia

    Andrew Lewis

  • Institut für Angewandte Informatik und Formale Beschreibungsverfahren - AIFB, Universität Karlsruhe, Karlsruhe, Germany

    Sanaz Mostaghim

  • Faculty of Business, Technology and Sustainable Development, Bond University, Gold Coast, Australia

    Marcus Randall

Bibliographic Information

  • Book Title: Biologically-Inspired Optimisation Methods

  • Book Subtitle: Parallel Algorithms, Systems and Applications

  • Editors: Andrew Lewis, Sanaz Mostaghim, Marcus Randall

  • Series Title: Studies in Computational Intelligence

  • DOI: https://doi.org/10.1007/978-3-642-01262-4

  • Publisher: Springer Berlin, Heidelberg

  • eBook Packages: Engineering, Engineering (R0)

  • Copyright Information: Springer-Verlag Berlin Heidelberg 2009

  • Hardcover ISBN: 978-3-642-01261-7Published: 25 May 2009

  • Softcover ISBN: 978-3-642-10177-9Published: 28 October 2010

  • eBook ISBN: 978-3-642-01262-4Published: 12 May 2009

  • Series ISSN: 1860-949X

  • Series E-ISSN: 1860-9503

  • Edition Number: 1

  • Number of Pages: XII, 360

  • Topics: Mathematical and Computational Engineering, Artificial Intelligence

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