Biologically-Inspired Optimisation Methods
Lewis, Andrew.
Biologically-Inspired Optimisation Methods Parallel Algorithms, Systems and Applications / [electronic resource] : edited by Andrew Lewis, Sanaz Mostaghim, Marcus Randall. - XII, 360 p. online resource. - Studies in Computational Intelligence, 210 1860-949X ; . - Studies in Computational Intelligence, 210 .
Evolution’s Niche in Multi-Criterion Problem Solving -- Applications of Parallel Platforms and Models in Evolutionary Multi-Objective Optimization -- Asynchronous Multi-Objective Optimisation in Unreliable Distributed Environments -- Dynamic Problems and Nature Inspired Meta-heuristics -- Relaxation Labelling Using Distributed Neural Networks -- Extremal Optimisation for Assignment Type Problems -- Niching for Ant Colony Optimisation -- Using Ant Colony Optimisation to Construct Meander-Line RFID Antennas -- The Radio Network Design Optimization Problem -- Strategies for Decentralised Balancing Power -- An Analysis of Dynamic Mutation Operators for Conformational Sampling -- Evolving Computer Chinese Chess Using Guided Learning.
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.
9783642012624
10.1007/978-3-642-01262-4 doi
Engineering.
Artificial intelligence.
Engineering mathematics.
Engineering.
Appl.Mathematics/Computational Methods of Engineering.
Artificial Intelligence (incl. Robotics).
TA329-348 TA640-643
519
Biologically-Inspired Optimisation Methods Parallel Algorithms, Systems and Applications / [electronic resource] : edited by Andrew Lewis, Sanaz Mostaghim, Marcus Randall. - XII, 360 p. online resource. - Studies in Computational Intelligence, 210 1860-949X ; . - Studies in Computational Intelligence, 210 .
Evolution’s Niche in Multi-Criterion Problem Solving -- Applications of Parallel Platforms and Models in Evolutionary Multi-Objective Optimization -- Asynchronous Multi-Objective Optimisation in Unreliable Distributed Environments -- Dynamic Problems and Nature Inspired Meta-heuristics -- Relaxation Labelling Using Distributed Neural Networks -- Extremal Optimisation for Assignment Type Problems -- Niching for Ant Colony Optimisation -- Using Ant Colony Optimisation to Construct Meander-Line RFID Antennas -- The Radio Network Design Optimization Problem -- Strategies for Decentralised Balancing Power -- An Analysis of Dynamic Mutation Operators for Conformational Sampling -- Evolving Computer Chinese Chess Using Guided Learning.
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.
9783642012624
10.1007/978-3-642-01262-4 doi
Engineering.
Artificial intelligence.
Engineering mathematics.
Engineering.
Appl.Mathematics/Computational Methods of Engineering.
Artificial Intelligence (incl. Robotics).
TA329-348 TA640-643
519