Machine Learning for Evolution Strategies / / by Oliver Kramer

• Autore: Kramer Oliver
• Pubblicazione: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016
• Edizione: 1st ed. 2016.
• Descrizione fisica: 1 online resource (IX, 124 p. 38 illus. in color.)
• Disciplina: 006.31
• Soggetto topico: Computational intelligence; Computer simulation; Data mining; System theory; Artificial intelligence; Computational Intelligence; Computer Modelling; Data Mining and Knowledge Discovery; Complex Systems; Artificial Intelligence
• Nota di bibliografia: Includes bibliographical references at the end of each chapters and index.
• Nota di contenuto: Part I Evolution Strategies -- Part II Machine Learning -- Part III Supervised Learning.
• Sommario/riassunto: 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.
• Titolo autorizzato: Machine Learning for Evolution Strategies
• ISBN: 3-319-33383-6
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910739403203321
• Lo trovi qui: Univ. Federico II
• Serie: Studies in Big Data, . 2197-6511 ; ; 20