Evolutionary Multiobjective Optimization with Gaussian Process Models
Language: English
Published by LAP LAMBERT Academic Publishing Jul 2015, 2015
- Softcover
- New
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermanyBuchWeltWeit Ludwig Meier e.K.
AbeBooks seller since January 11, 2012
Condition: New
£ 47.36
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Add to basketItem description from seller
This item is printed on demand - it takes 3-4 days longer - Neuware -This book focuses on the field of surrogate-model-based multiobjective evolutionary optimization. It describes the sate-of-the-art concepts and methods, presents various optimization problems and describes current challenges. The main contributions are done for the optimization problems, where solutions are presented with uncertainty. To compare solutions under uncertainty and improve the optimization results the new relations for comparing solutions under uncertainty are defined. These relations reduce the possibility of incorrect comparisons due to the inaccurate approximations. The relations under uncertainty are then used in the new surrogate-model-based multiobjective evolutionary algorithm called GP-DEMO. The algorithm is thoroughly tested on benchmark and real-world problems and the results show that GP-DEMO, in comparison to other multiobjective evolutionary algorithms, produces comparable results while requiring fewer exact evaluations of the original objective functions. 116 pp. Englisch.…
Seller Inventory # 9783659759352
- Title
- Evolutionary Multiobjective Optimization with Gaussian Process Models
- Author
- Miha Mlakar
- Publisher
- LAP LAMBERT Academic Publishing Jul 2015
- Publication year
- 2015
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 365975935X
- ISBN 13
- 9783659759352
- Item weight
- 191 grams
- Dimensions
- 220x150x8 mm
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About the Author
"About the title" may belong to another edition of this title.
BuchWeltWeit Ludwig Meier e.K.
Bergisch Gladbach, Germany
AbeBooks seller since January 11, 2012
Shipping rates from Germany to U.S.A.
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