High-Performance Simulation-Based Optimization /
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| Imprint: | Cham : Springer, 2020. |
|---|---|
| Description: | 1 online resource (xiii, 291 pages) : illustrations (some color) |
| Language: | English |
| Series: | Studies in computational intelligence ; volume 833 Studies in computational intelligence ; v. 833. |
| Subject: | |
| Format: | E-Resource Book |
| URL for this record: | http://pi.lib.uchicago.edu/1001/cat/bib/12601507 |
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| 245 | 0 | 0 | |a High-Performance Simulation-Based Optimization / |c Thomas Bartz-Beielstein, Bogdan Filipič, Peter Korošec, El-Ghazali Talbi, editors. |
| 264 | 1 | |a Cham : |b Springer, |c 2020. | |
| 300 | |a 1 online resource (xiii, 291 pages) : |b illustrations (some color) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 490 | 1 | |a Studies in computational intelligence ; |v volume 833 | |
| 505 | 0 | |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. | |
| 520 | |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. | ||
| 650 | 0 | |a Mathematical optimization. |0 http://id.loc.gov/authorities/subjects/sh85082127 | |
| 650 | 0 | |a Simulation methods. |0 http://id.loc.gov/authorities/subjects/sh85122767 | |
| 650 | 7 | |a Mathematical optimization. |2 fast |0 (OCoLC)fst01012099 | |
| 650 | 7 | |a Simulation methods. |2 fast |0 (OCoLC)fst01119166 | |
| 655 | 4 | |a Electronic books. | |
| 700 | 1 | |a Bartz-Beielstein, Thomas. | |
| 700 | 1 | |a Filipič, Bogdan. | |
| 700 | 1 | |a Korošec, Peter. | |
| 700 | 1 | |a Talbi, El-Ghazali. | |
| 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. |0 http://id.loc.gov/authorities/names/no2005104439 | |
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| 928 | |t Library of Congress classification |a QA402.5 |l Online |c UC-FullText |u https://link.springer.com/10.1007/978-3-030-18764-4 |z Springer Nature |g ebooks |i 12617114 | ||