Buch, Englisch, 124 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 3317 g
Reihe: Studies in Big Data
Buch, Englisch, 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
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 Daten / Datenbanken Data Mining
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
- Naturwissenschaften Physik Angewandte Physik Soziophysik, Wirtschaftsphysik
- 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.
