Introduction to Learning Classifier Systems

• Κύριοι συγγραφείς: Urbanowicz, Ryan J. (Συγγραφέας), Browne, Will N. (Συγγραφέας)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2017.
• Σειρά: SpringerBriefs in Intelligent Systems, Artificial Intelligence, Multiagent Systems, and Cognitive Robotics,
• Θέματα: Computer science.; Computers.; Artificial intelligence.; Bioinformatics.; Mathematical optimization.; Computational intelligence.; Control engineering.; Robotics.; Mechatronics.; Computer Science.; Artificial Intelligence (incl. Robotics).; Computational Intelligence.; Optimization.; Computational Biology/Bioinformatics.; Control, Robotics, Mechatronics.; Theory of Computation.

 This accessible introduction shows the reader how to understand, implement, adapt, and apply Learning Classifier Systems (LCSs) to interesting and difficult problems. The text builds an understanding from basic ideas and concepts. The authors first explore learning through environment interaction, and then walk through the components of LCS that form this rule-based evolutionary algorithm. The applicability and adaptability of these methods is highlighted by providing descriptions of common methodological alternatives for different components that are suited to different types of problems from data mining to autonomous robotics. The authors have also paired exercises and a simple educational LCS (eLCS) algorithm (implemented in Python) with this book. It is suitable for courses or self-study by advanced undergraduate and postgraduate students in subjects such as Computer Science, Engineering, Bioinformatics, and Cybernetics, and by researchers, data analysts, and machine learning practitioners. 

 LCSs in a Nutshell -- LCS Concepts -- Functional Cycle Components -- LCS Adaptability -- Applying LCSs.