Machine Learning for Evolution Strategies

• Κύριος συγγραφέας: Kramer, Oliver (Συγγραφέας)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2016.
• Σειρά: Studies in Big Data, 20
• Θέματα: Engineering.; Data mining.; Artificial intelligence.; Computer simulation.; Sociophysics.; Econophysics.; Computational intelligence.; Computational Intelligence.; Simulation and Modeling.; Data Mining and Knowledge Discovery.; Socio- and Econophysics, Population and Evolutionary Models.; Artificial Intelligence (incl. Robotics).

 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. 

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