Machine Learning for Evolution Strategies

Oliver Kramer

ISBN 10: 3319815008 / ISBN 13: 9783319815008

Editore: Springer, Berlin, Springer International Publishing, Springer, 2018

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Druck auf Anfrage Neuware - Printed after ordering - This bookintroduces numerous algorithmic hybridizations between both worlds that showhow machine learning can improve and support evolution strategies. The set ofmethods comprises covariance matrix estimation, meta-modeling of fitness andconstraint functions, dimensionality reduction for search and visualization ofhigh-dimensional optimization processes, and clustering-based niching. Aftergiving an introduction to evolution strategies and machine learning, the bookbuilds the bridge between both worlds with an algorithmic and experimentalperspective. Experiments mostly employ a (1+1)-ES and are implemented in Pythonusing the machine learning library scikit-learn. The examples are conducted ontypical benchmark problems illustrating algorithmic concepts and theirexperimental behavior. The book closes with a discussion of related lines ofresearch.

Codice articolo 9783319815008

Dati bibliografici

• Titolo: Machine Learning for Evolution Strategies
• Editore: Springer, Berlin, Springer International Publishing, Springer
• Data di pubblicazione: 2018
• Lingua: Inglese
• ISBN 10: 3319815008
• ISBN 13: 9783319815008
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 235 L x 155 A x 7 P (millimetri) 235 millimetri di larghezza per 155 millimetri di altezza per 7 millimetri di profondità
• Peso: 219 grammi

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Riassunto

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.