Machine Learning for Evolution Strategies (Record no. 79823)
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fixed length control field | 03016nam a22005895i 4500 |
001 - CONTROL NUMBER | |
control field | 978-3-319-33383-0 |
005 - DATE AND TIME OF LATEST TRANSACTION | |
control field | 20220801221559.0 |
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
fixed length control field | 160525s2016 sz | s |||| 0|eng d |
020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
ISBN | 9783319333830 |
-- | 978-3-319-33383-0 |
082 04 - CLASSIFICATION NUMBER | |
Call Number | 006.3 |
100 1# - AUTHOR NAME | |
Author | Kramer, Oliver. |
245 10 - TITLE STATEMENT | |
Title | Machine Learning for Evolution Strategies |
250 ## - EDITION STATEMENT | |
Edition statement | 1st ed. 2016. |
300 ## - PHYSICAL DESCRIPTION | |
Number of Pages | IX, 124 p. 38 illus. in color. |
490 1# - SERIES STATEMENT | |
Series statement | Studies in Big Data, |
505 0# - FORMATTED CONTENTS NOTE | |
Remark 2 | Part I Evolution Strategies -- Part II Machine Learning -- Part III Supervised Learning. |
520 ## - SUMMARY, ETC. | |
Summary, etc | 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. |
856 40 - ELECTRONIC LOCATION AND ACCESS | |
Uniform Resource Identifier | https://doi.org/10.1007/978-3-319-33383-0 |
942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
Koha item type | eBooks |
264 #1 - | |
-- | Cham : |
-- | Springer International Publishing : |
-- | Imprint: Springer, |
-- | 2016. |
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-- | computer |
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338 ## - | |
-- | online resource |
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347 ## - | |
-- | text file |
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650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Computational intelligence. |
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Computer simulation. |
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Data mining. |
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | System theory. |
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Artificial intelligence. |
650 14 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Computational Intelligence. |
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Computer Modelling. |
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Data Mining and Knowledge Discovery. |
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Complex Systems. |
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Artificial Intelligence. |
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
-- | 2197-6511 ; |
912 ## - | |
-- | ZDB-2-ENG |
912 ## - | |
-- | ZDB-2-SXE |
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