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on1105192958 |
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OCoLC |
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20240909213021.0 |
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m o d |
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190603s2020 sz a o 000 0 eng d |
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|a 019431624
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|a 1103463794
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|a 9783030187644
|q (electronic bk.)
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|a (OCoLC)1105192958
|z (OCoLC)1103463794
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|a com.springer.onix.9783030187644
|b Springer Nature
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4 |
|a QA402.5
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| 049 |
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|a HCDD
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| 245 |
0 |
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|a High-Performance Simulation-Based Optimization /
|c Thomas Bartz-Beielstein, Bogdan Filipič, Peter Korošec, El-Ghazali Talbi, editors.
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| 264 |
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1 |
|a Cham :
|b Springer,
|c 2020.
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| 300 |
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|a 1 online resource (xiii, 291 pages) :
|b illustrations (some color)
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| 336 |
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 490 |
1 |
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|a Studies in computational intelligence ;
|v volume 833
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| 505 |
0 |
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|a Infill Criteria for Multiobjective Bayesian Optimization -- Many-Objective Optimization with Limited Computing Budget -- Multi-Objective Bayesian Optimization for Engineering Simulation -- Automatic Configuration of Multi-Objective Optimizers and Multi-Objective Configuration -- Optimization and Visualization in Many-Objective Space Trajectory Design -- Simulation Optimization through Regression or Kriging Metamodels -- Towards Better Integration of Surrogate Models and Optimizers -- Surrogate-Assisted Evolutionary Optimization of Large Problems -- Overview and Comparison of Gaussian Process-Based Surrogate Models for Mixed Continuous and Discrete Variables: Application on Aerospace Design Problems -- Open Issues in Surrogate-Assisted Optimization -- A Parallel Island Model for Hypervolume-Based Many-Objective Optimization -- Many-Core Branch-and-Bound for GPU Accelerators and MIC Coprocessors.
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| 520 |
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|a This book presents the state of the art in designing high-performance algorithms that combine simulation and optimization in order to solve complex optimization problems in science and industry, problems that involve time-consuming simulations and expensive multi-objective function evaluations. As traditional optimization approaches are not applicable per se, combinations of computational intelligence, machine learning, and high-performance computing methods are popular solutions. But finding a suitable method is a challenging task, because numerous approaches have been proposed in this highly dynamic field of research. Thats where this book comes in: It covers both theory and practice, drawing on the real-world insights gained by the contributing authors, all of whom are leading researchers. Given its scope, if offers a comprehensive reference guide for researchers, practitioners, and advanced-level students interested in using computational intelligence and machine learning to solve expensive optimization problems.
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| 650 |
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|a Mathematical optimization.
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| 650 |
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0 |
|a Simulation methods.
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| 650 |
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7 |
|a simulation methods.
|2 aat
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| 650 |
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7 |
|a Mathematical optimization
|2 fast
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| 650 |
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7 |
|a Simulation methods
|2 fast
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| 700 |
1 |
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|a Bartz-Beielstein, Thomas.
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| 700 |
1 |
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|a Filipič, Bogdan.
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| 700 |
1 |
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|a Korošec, Peter.
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| 700 |
1 |
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|a Talbi, El-Ghazali.
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| 758 |
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|i has work:
|a High-performance simulation-based optimization (Text)
|1 https://id.oclc.org/worldcat/entity/E39PCFC9hHbgQH348KRp6fmgqP
|4 https://id.oclc.org/worldcat/ontology/hasWork
|
| 776 |
0 |
8 |
|i Print version:
|t High-Performance Simulation-Based Optimization.
|d Cham : Springer, 2020
|z 3030187632
|z 9783030187637
|w (OCoLC)1090831040
|
| 830 |
|
0 |
|a Studies in computational intelligence ;
|v v. 833.
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| 856 |
4 |
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|u https://holycross.idm.oclc.org/login?auth=cas&url=https://link.springer.com/10.1007/978-3-030-18764-4
|y Click for online access
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| 903 |
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|a SPRING-ROBOTICS2020
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| 994 |
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|a 92
|b HCD
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