Ebook: Biologically-Inspired Optimisation Methods: Parallel Algorithms, Systems and Applications

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.

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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.

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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.
Content:
Front Matter....Pages -
Evolution’s Niche in Multi-Criterion Problem Solving....Pages 1-21
Applications of Parallel Platforms and Models in Evolutionary Multi-Objective Optimization....Pages 23-49
Asynchronous Multi-Objective Optimisation in Unreliable Distributed Environments....Pages 51-78
Dynamic Problems and Nature Inspired Meta-heuristics....Pages 79-109
Relaxation Labelling Using Distributed Neural Networks....Pages 111-138
Extremal Optimisation for Assignment Type Problems....Pages 139-164
Niching for Ant Colony Optimisation....Pages 165-188
Using Ant Colony Optimisation to Construct Meander-Line RFID Antennas....Pages 189-217
The Radio Network Design Optimization Problem....Pages 219-260
Strategies for Decentralised Balancing Power....Pages 261-289
An Analysis of Dynamic Mutation Operators for Conformational Sampling....Pages 291-323
Evolving Computer Chinese Chess Using Guided Learning....Pages 325-354
Back Matter....Pages -

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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.
Content:
Front Matter....Pages -
Evolution’s Niche in Multi-Criterion Problem Solving....Pages 1-21
Applications of Parallel Platforms and Models in Evolutionary Multi-Objective Optimization....Pages 23-49
Asynchronous Multi-Objective Optimisation in Unreliable Distributed Environments....Pages 51-78
Dynamic Problems and Nature Inspired Meta-heuristics....Pages 79-109
Relaxation Labelling Using Distributed Neural Networks....Pages 111-138
Extremal Optimisation for Assignment Type Problems....Pages 139-164
Niching for Ant Colony Optimisation....Pages 165-188
Using Ant Colony Optimisation to Construct Meander-Line RFID Antennas....Pages 189-217
The Radio Network Design Optimization Problem....Pages 219-260
Strategies for Decentralised Balancing Power....Pages 261-289
An Analysis of Dynamic Mutation Operators for Conformational Sampling....Pages 291-323
Evolving Computer Chinese Chess Using Guided Learning....Pages 325-354
Back Matter....Pages -
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