Machine Learning for Evolution Strategies

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.

Table des matières

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

Détails du produit

• Auteurs: Oliver Kramer
• Edition: Springer, Berlin
• Langues: Anglais
• Format d'édition: Livre de poche
• Sortie: 01.01.2018
• EAN: 9783319815008
• ISBN: 978-3-31-981500-8
• Pages: 124
• Dimensions: 155 mm x 7 mm x 235 mm
• Poids: 219 g
• Illustrations: IX, 124 p. 38 illus. in color.
• Thèmes: Studies in Big Data; Studies in Big Data
• Catégories: Sciences naturelles, médecine, informatique, technique > Technique > Général, dictionnaires; B, Data Mining, Artificial Intelligence, engineering, Complex systems, Ecological science, the Biosphere, Social research & statistics, Data Mining and Knowledge Discovery, Computational Intelligence, Computer simulation, Computer modelling & simulation, Simulation and Modeling, Expert systems / knowledge-based systems, Econophysics, Sociophysics, Data-driven Science, Modeling and Theory Building, Computer Modelling