Kramer Machine Learning for Evolution Strategies

1. Auflage 2016
ISBN: 978-3-319-33383-0
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 20, 124 Seiten, eBook

Reihe: Studies in Big Data

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.

Kramer, Oliver

Inhaltsverzeichnis

Part I Evolution Strategies.- Part II Machine Learning.- Part III Supervised Learning.