大葉大學圖書館 |

Machine learning for evolution strategies[electronic resource] /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
杜威分類號:	006.31
書名/作者:	Machine learning for evolution strategies/ by Oliver Kramer.
作者:	Kramer, Oliver.
出版者:	Cham : : Springer International Publishing :, 2016.
面頁冊數:	ix, 124 p. : : ill., digital ;; 24 cm.
Contained By:	Springer eBooks
標題:	Machine learning.
標題:	Engineering.
標題:	Computational Intelligence.
標題:	Simulation and Modeling.
標題:	Data Mining and Knowledge Discovery.
標題:	Socio- and Econophysics, Population and Evolutionary Models.
標題:	Artificial Intelligence (incl. Robotics)
ISBN:	9783319333830
ISBN:	9783319333816
內容註:	Part I Evolution Strategies -- Part II Machine Learning -- Part III Supervised Learning.
摘要、提要註:	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.
電子資源:	http://dx.doi.org/10.1007/978-3-319-33383-0

Machine learning for evolution strategies[electronic resource] /
Kramer, Oliver.

Machine learning for evolution strategies[electronic resource] /by Oliver Kramer. - Cham :Springer International Publishing :2016. - ix, 124 p. :ill., digital ;24 cm. - Studies in big data,v.202197-6503 ;. - Studies in big data ;v.7..

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.

ISBN: 9783319333830

Standard No.: 10.1007/978-3-319-33383-0doiSubjects--Topical Terms:

202931
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Machine learning for evolution strategies[electronic resource] /
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