Machine Learning for Evolution Strategies

Nonfiction, Computers, Advanced Computing, Artificial Intelligence, General Computing
Cover of the book Machine Learning for Evolution Strategies by Oliver Kramer, Springer International Publishing
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: Oliver Kramer ISBN: 9783319333830
Publisher: Springer International Publishing Publication: May 25, 2016
Imprint: Springer Language: English
Author: Oliver Kramer
ISBN: 9783319333830
Publisher: Springer International Publishing
Publication: May 25, 2016
Imprint: Springer
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

More books from Springer International Publishing

Cover of the book Advances in Condition Monitoring of Machinery in Non-Stationary Operations by Oliver Kramer
Cover of the book Computational Photonic Sensors by Oliver Kramer
Cover of the book Compression Garments in Sports: Athletic Performance and Recovery by Oliver Kramer
Cover of the book Mathematics, Informatics, and Their Applications in Natural Sciences and Engineering by Oliver Kramer
Cover of the book Intelligent Medical Decision Support System Based on Imperfect Information by Oliver Kramer
Cover of the book Multi-Agent Based Simulation XVIII by Oliver Kramer
Cover of the book Advances in Information and Communication by Oliver Kramer
Cover of the book Handbook of Sepsis by Oliver Kramer
Cover of the book Integrated Circuit Authentication by Oliver Kramer
Cover of the book Turing Machine Universality of the Game of Life by Oliver Kramer
Cover of the book Advances in the Application of Lasers in Materials Science by Oliver Kramer
Cover of the book Business Intelligence by Oliver Kramer
Cover of the book Quantum Mechanics for Pedestrians 1: Fundamentals by Oliver Kramer
Cover of the book Cultural Tourism in a Digital Era by Oliver Kramer
Cover of the book Heritage in Action by Oliver Kramer
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy