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Machine learning for evolution strategies [electronic resource]

자료유형

E-Book(소장)

개인저자

Kramer, Oliver.

서명 / 저자사항

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

발행사항

Cham : Springer International Publishing : Imprint: Springer, c2016.

형태사항

1 online resource (ix, 124 p.) : col. ill.

총서사항

Studies in big data,2197-6503 ; 20

ISBN

9783319333830

요약

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.

일반주기

Title from e-Book title page.

내용주기

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

서지주기

Includes bibliographical references and index.

이용가능한 다른형태자료

Issued also as a book.

일반주제명

Engineering. Machine learning.

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100	1	▼a Kramer, Oliver.
245	1 0	▼a Machine learning for evolution strategies ▼h [electronic resource] / ▼c Oliver Kramer.
260		▼a Cham : ▼b Springer International Publishing : ▼b Imprint: Springer, ▼c c2016.
300		▼a 1 online resource (ix, 124 p.) : ▼b col. ill.
490	1	▼a Studies in big data, ▼x 2197-6503 ; ▼v 20
500		▼a Title from e-Book title page.
504		▼a Includes bibliographical references and index.
505	0	▼a Part I Evolution Strategies -- Part II Machine Learning -- Part III Supervised Learning.
520		▼a 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.
530		▼a Issued also as a book.
538		▼a Mode of access: World Wide Web.
650	0	▼a Engineering.
650	0	▼a Machine learning.
830	0	▼a Studies in big data ; ▼v 20.
856	4 0	▼u https://oca.korea.ac.kr/link.n2s?url=http://dx.doi.org/10.1007/978-3-319-33383-0
945		▼a KLPA
991		▼a E-Book(소장)

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No.	소장처	청구기호	등록번호	도서상태	반납예정일	예약	서비스
No. 1	소장처 중앙도서관/e-Book 컬렉션/	청구기호 CR 006.31	등록번호 E14022355	도서상태 대출불가(열람가능)	반납예정일	예약	서비스 M

Machine learning for evolution strategies [electronic resource]

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