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.

Résumé

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.

Détails du produit

• Auteurs: Oliver Kramer
• Edition: Springer, Berlin
• Langues: Anglais
• Format d'édition: Livre Relié
• Sortie: 01.01.2016
• EAN: 9783319333816
• ISBN: 978-3-31-933381-6
• Pages: 124
• Dimensions: 162 mm x 242 mm x 14 mm
• Poids: 321 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