Machine Learning for Evolution Strategies

1. Auflage 2016
ISBN: 978-3-319-33381-6
Verlag: Springer International Publishing

Buch, Englisch, Band 20, 124 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 3317 g

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

Fachgebiete

• Mathematik | Informatik EDV | Informatik Professionelle Anwendung Computersimulation & Modelle, 3-D Graphik
• Naturwissenschaften Physik Angewandte Physik Soziophysik, Wirtschaftsphysik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Technische Wissenschaften Technik Allgemein Modellierung & Simulation
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining

Inhaltsverzeichnis

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