Details

Title Computational Intelligence in Expensive Optimization Problems
Author Tenne, Yoel (Editor)/ Goh, Chi-keong (Editor)
Binding Paperback
Edition number 2010th
Edition 2010
Condition New
Pages 800
Volumes 1
Language ENG
Publisher Springer Verlag
Publication date 2012
Illustrated Yes
Features Illustrated
Bookseller's Inventory # x-3642263186
ISBN 9783642263187 / 3642263186
Weight 2.3 lbs (1.04 kg)
Dimensions 9 x 5.9 x 1.6 in (22.86 x 14.99 x 4.06 cm)
Category Mathematics
Dewey Decimal Code 519.6
Quantity available 2

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From the publisher

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.

From the rear cover

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.

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