132,95 €*
Versandkostenfrei per Post / DHL
Lieferzeit 4-7 Werktage
This is followed by a presentation of a variety of data-driven single- and multi-objective optimization algorithms that seamlessly integrate modern machine learning such as deep learning and transfer learning with evolutionary and swarm optimization algorithms. Applications of data-driven optimization ranging from aerodynamic design, optimization of industrial processes, to deep neural architecture search are included.
This is followed by a presentation of a variety of data-driven single- and multi-objective optimization algorithms that seamlessly integrate modern machine learning such as deep learning and transfer learning with evolutionary and swarm optimization algorithms. Applications of data-driven optimization ranging from aerodynamic design, optimization of industrial processes, to deep neural architecture search are included.
Includes a brief introduction to mathematical programming, metaheuristic algorithms, and machine learning techniques
Presents a systematic description of most recent research advances in data-driven evolutionary optimization, including surrogate-assisted single-, multi-, and many-objective optimization
Introduces various intuitive and mathematical surrogate management strategies, such as the trust region method and acquisition functions in Bayesian optimization
Provides applications of data-driven optimization to engineering design, automation of process industry, health care, and automated machine learning
| Erscheinungsjahr: | 2022 |
|---|---|
| Fachbereich: | Technik allgemein |
| Genre: | Technik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Seiten: | 420 |
| Reihe: | Studies in Computational Intelligence |
| Inhalt: |
xxv
393 S. 83 s/w Illustr. 76 farbige Illustr. 393 p. 159 illus. 76 illus. in color. |
| ISBN-13: | 9783030746421 |
| ISBN-10: | 3030746429 |
| Sprache: | Englisch |
| Ausstattung / Beilage: | Paperback |
| Einband: | Kartoniert / Broschiert |
| Autor: |
Jin, Yaochu
Sun, Chaoli Wang, Handing |
| Auflage: | 1st ed. 2021 |
| Hersteller: |
Springer International Publishing
Springer International Publishing AG Studies in Computational Intelligence |
| Maße: | 235 x 155 x 23 mm |
| Von/Mit: | Yaochu Jin (u. a.) |
| Erscheinungsdatum: | 30.06.2022 |
| Gewicht: | 0,633 kg |
Includes a brief introduction to mathematical programming, metaheuristic algorithms, and machine learning techniques
Presents a systematic description of most recent research advances in data-driven evolutionary optimization, including surrogate-assisted single-, multi-, and many-objective optimization
Introduces various intuitive and mathematical surrogate management strategies, such as the trust region method and acquisition functions in Bayesian optimization
Provides applications of data-driven optimization to engineering design, automation of process industry, health care, and automated machine learning
| Erscheinungsjahr: | 2022 |
|---|---|
| Fachbereich: | Technik allgemein |
| Genre: | Technik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Seiten: | 420 |
| Reihe: | Studies in Computational Intelligence |
| Inhalt: |
xxv
393 S. 83 s/w Illustr. 76 farbige Illustr. 393 p. 159 illus. 76 illus. in color. |
| ISBN-13: | 9783030746421 |
| ISBN-10: | 3030746429 |
| Sprache: | Englisch |
| Ausstattung / Beilage: | Paperback |
| Einband: | Kartoniert / Broschiert |
| Autor: |
Jin, Yaochu
Sun, Chaoli Wang, Handing |
| Auflage: | 1st ed. 2021 |
| Hersteller: |
Springer International Publishing
Springer International Publishing AG Studies in Computational Intelligence |
| Maße: | 235 x 155 x 23 mm |
| Von/Mit: | Yaochu Jin (u. a.) |
| Erscheinungsdatum: | 30.06.2022 |
| Gewicht: | 0,633 kg |
Ähnliche Produkte
Lieferzeit 2-3 Wochen
Lieferzeit 4-7 Werktage
Lieferzeit 1-2 Wochen
Lieferzeit 1-2 Wochen
Aktuell nicht verfügbar
Lieferzeit 1-2 Wochen
