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Machine Learning for Evolution Strategies [electronic resource] / by Oliver Kramer.

By: Kramer, Oliver [author.].
Contributor(s): SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Studies in Big Data: 20Publisher: Cham : Springer International Publishing : Imprint: Springer, 2016Edition: 1st ed. 2016.Description: IX, 124 p. 38 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319333830.Subject(s): Computational intelligence | Computer simulation | Data mining | System theory | Artificial intelligence | Computational Intelligence | Computer Modelling | Data Mining and Knowledge Discovery | Complex Systems | Artificial IntelligenceAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 006.3 Online resources: Click here to access online
Contents:
Part I Evolution Strategies -- Part II Machine Learning -- Part III Supervised Learning.
In: Springer Nature eBookSummary: 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.
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Part I Evolution Strategies -- Part II Machine Learning -- Part III Supervised Learning.

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.

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