Kramer, Oliver.
Machine Learning for Evolution Strategies [electronic resource] / by Oliver Kramer. - IX, 124 p. 38 illus. in color. online resource. - Studies in Big Data, 20 2197-6503 ; . - Studies in Big Data, 20 .
Part I Evolution Strategies -- Part II Machine Learning -- Part III Supervised Learning.
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.
9783319333830
10.1007/978-3-319-33383-0 doi
Engineering.
Data mining.
Artificial intelligence.
Computer simulation.
Sociophysics.
Econophysics.
Computational intelligence.
Engineering.
Computational Intelligence.
Simulation and Modeling.
Data Mining and Knowledge Discovery.
Socio- and Econophysics, Population and Evolutionary Models.
Artificial Intelligence (incl. Robotics).
Q342
006.3
Machine Learning for Evolution Strategies [electronic resource] / by Oliver Kramer. - IX, 124 p. 38 illus. in color. online resource. - Studies in Big Data, 20 2197-6503 ; . - Studies in Big Data, 20 .
Part I Evolution Strategies -- Part II Machine Learning -- Part III Supervised Learning.
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.
9783319333830
10.1007/978-3-319-33383-0 doi
Engineering.
Data mining.
Artificial intelligence.
Computer simulation.
Sociophysics.
Econophysics.
Computational intelligence.
Engineering.
Computational Intelligence.
Simulation and Modeling.
Data Mining and Knowledge Discovery.
Socio- and Econophysics, Population and Evolutionary Models.
Artificial Intelligence (incl. Robotics).
Q342
006.3