Overview
- Presents recent research in Biologically-inspired Optimisation Methods
Part of the book series: Studies in Computational Intelligence (SCI, volume 210)
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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.
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Keywords
Table of contents (12 chapters)
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Front Matter
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Back Matter
Editors and Affiliations
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