Overview
- First book to introduce the emerging field of computational intelligence in expensive optimization problems
- Provides both theoretical treatments and real-world insights gained by experience in computational intelligence in expensive optimization problems
Part of the book series: Adaptation, Learning, and Optimization (ALO, volume 2)
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Table of contents (26 chapters)
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Front Matter
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Techniques for Resource-Intensive Problems
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Front Matter
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Techniques for High-Dimensional Problems
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Front Matter
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Keywords
About this book
In modern science and engineering, laboratory experiments are replaced by high fidelity and computationally expensive simulations. Using such simulations reduces costs and shortens development times but introduces new challenges to design optimization process. Examples of such challenges include limited computational resource for simulation runs, complicated response surface of the simulation inputs-outputs, and etc.
Under such difficulties, classical optimization and analysis methods may perform poorly. This motivates the application of computational intelligence methods such as evolutionary algorithms, neural networks and fuzzy logic, which often perform well in such settings. This is the first book to introduce the emerging field of computational intelligence in expensive optimization problems. Topics covered include: dedicated implementations of evolutionary algorithms, neural networks and fuzzy logic. reduction of expensive evaluations (modelling, variable-fidelity, fitness inheritance), frameworks for optimization (model management, complexity control, model selection), parallelization of algorithms (implementation issues on clusters, grids, parallel machines), incorporation of expert systems and human-system interface, single and multiobjective algorithms, data mining and statistical analysis, analysis of real-world cases (such as multidisciplinary design optimization).
The edited book provides both theoretical treatments and real-world insights gained by experience, all contributed by leading researchers in the respective fields. As such, it is a comprehensive reference for researchers, practitioners, and advanced-level students interested in both the theory and practice of using computational intelligence for expensive optimization problems.
Editors and Affiliations
Bibliographic Information
Book Title: Computational Intelligence in Expensive Optimization Problems
Editors: Yoel Tenne, Chi-Keong Goh
Series Title: Adaptation, Learning, and Optimization
DOI: https://doi.org/10.1007/978-3-642-10701-6
Publisher: Springer Berlin, Heidelberg
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer-Verlag Berlin Heidelberg 2010
Hardcover ISBN: 978-3-642-10700-9Published: 22 April 2010
Softcover ISBN: 978-3-642-26318-7Published: 28 May 2012
eBook ISBN: 978-3-642-10701-6Published: 10 March 2010
Series ISSN: 1867-4534
Series E-ISSN: 1867-4542
Edition Number: 1
Number of Pages: 800
Number of Illustrations: 270 b/w illustrations
Topics: Mathematical and Computational Engineering, Artificial Intelligence, Applications of Mathematics