Machine Learning for Evolution Strategies
Springer International Publishing (Verlag)
978-3-319-33381-6 (ISBN)
This bookintroduces numerous algorithmic hybridizations between both worlds that showhow machine learning can improve and support evolution strategies. The set ofmethods comprises covariance matrix estimation, meta-modeling of fitness andconstraint functions, dimensionality reduction for search and visualization ofhigh-dimensional optimization processes, and clustering-based niching. Aftergiving an introduction to evolution strategies and machine learning, the bookbuilds the bridge between both worlds with an algorithmic and experimentalperspective. Experiments mostly employ a (1+1)-ES and are implemented in Pythonusing the machine learning library scikit-learn. The examples are conducted ontypical benchmark problems illustrating algorithmic concepts and theirexperimental behavior. The book closes with a discussion of related lines ofresearch.
Part I Evolution Strategies.- Part II Machine Learning.- Part III Supervised Learning.
| Erscheinungsdatum | 08.10.2016 |
|---|---|
| Reihe/Serie | Studies in Big Data |
| Zusatzinfo | IX, 124 p. 38 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
| Technik | |
| Schlagworte | artificial intelligence (incl. robotics) • Big Data • Computational Intelligence • Data-driven Science, Modeling and Theory Building • Data Mining • data mining and knowledge discovery • Engineering • evolutionary computation • Evolution Strategies • machine learning • Simulation and modeling • Socio- and Econophysics, Population and Evolutiona |
| ISBN-10 | 3-319-33381-X / 331933381X |
| ISBN-13 | 978-3-319-33381-6 / 9783319333816 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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