Buch, Englisch, Band 20, 124 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 3317 g
Reihe: Studies in Big Data
Buch, Englisch, Band 20, 124 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 3317 g
Reihe: Studies in Big Data
ISBN: 978-3-319-33381-6
Verlag: Springer International Publishing
This book introduces numerous algorithmic hybridizations between both worlds that show how machine learning can improve and support evolution strategies. The set of methods comprises covariance matrix estimation, meta-modeling of fitness and constraint functions, dimensionality reduction for search and visualization of high-dimensional optimization processes, and clustering-based niching. After giving an introduction to evolution strategies and machine learning, the book builds the bridge between both worlds with an algorithmic and experimental perspective. Experiments mostly employ a (1+1)-ES and are implemented in Python using the machine learning library scikit-learn. The examples are conducted on typical benchmark problems illustrating algorithmic concepts and their experimental behavior. The book closes with a discussion of related lines of research.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
- Naturwissenschaften Physik Angewandte Physik Soziophysik, Wirtschaftsphysik
- Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining
- Technische Wissenschaften Technik Allgemein Modellierung & Simulation
- Mathematik | Informatik EDV | Informatik Professionelle Anwendung Computersimulation & Modelle, 3-D Graphik
Weitere Infos & Material
Part I Evolution Strategies.- Part II Machine Learning.- Part III Supervised Learning.
