Machine Learning for Evolution Strategies (Record no. 51683)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02792nam a22005535i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 978-3-319-33383-0 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20200420220217.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 160525s2016 gw | 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 |
| 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 | http://dx.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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| -- | text |
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| -- | computer |
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| -- | rdamedia |
| 338 ## - | |
| -- | online resource |
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| 347 ## - | |
| -- | text file |
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| -- | rda |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Engineering. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Data mining. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Artificial intelligence. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computer simulation. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Sociophysics. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Econophysics. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computational intelligence. |
| 650 14 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Engineering. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computational Intelligence. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Simulation and Modeling. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Data Mining and Knowledge Discovery. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Socio- and Econophysics, Population and Evolutionary Models. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Artificial Intelligence (incl. Robotics). |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
| -- | 2197-6503 ; |
| 912 ## - | |
| -- | ZDB-2-ENG |
No items available.