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Beschreibung
This book is designed as a textbook, suitable for self-learning or for teaching an upper-year university course on derivative-free and blackbox optimization.
The book is split into 5 parts and is designed to be modular; any individual part depends only on the material in Part I. Part I of the book discusses what is meant by Derivative-Free and Blackbox Optimization, provides background material, and early basics while Part II focuses on heuristic methods (Genetic Algorithms and Nelder-Mead). Part III presents direct search methods (Generalized Pattern Search and Mesh Adaptive Direct Search) and Part IV focuses on model-based methods (Simplex Gradient and Trust Region). Part V discusses dealing with constraints, using surrogates, and bi-objective optimization.
End of chapter exercises are included throughout as well as 15 end of chapter projects and over 40 figures. Benchmarking techniques are also presented in the appendix.
Details
| Verlag | Springer International Publishing |
| Ersterscheinung | 04. September 2018 |
| Maße | 23.5 cm x 15.5 cm |
| Gewicht | 546 Gramm |
| Format | Softcover |
| ISBN-13 | 9783319886800 |
| Auflage | Softcover reprint of the original 1st ed. 2017 |
| Seiten | 302 |
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