Biologically-Inspired Optimisation Methods

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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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.

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

• 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

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