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Introduction to Learning Classifier Systems / by Ryan J. Urbanowicz, Will N. Browne.
- Format:
- Book
- Author/Creator:
- Urbanowicz, Ryan J., author.
- Browne, Will N., author.
- Series:
- Computer Science (Springer-11645)
- SpringerBriefs in Intelligent Systems, Artificial Intelligence, Multiagent Systems, and Cognitive Robotics,. 2196-548X
- SpringerBriefs in Intelligent Systems, Artificial Intelligence, Multiagent Systems, and Cognitive Robotics, 2196-548X
- Language:
- English
- Subjects (All):
- Artificial intelligence.
- Computational intelligence.
- Mathematical optimization.
- Bioinformatics.
- Automatic control.
- Robotics.
- Mechatronics.
- Computers.
- Artificial Intelligence.
- Computational Intelligence.
- Optimization.
- Computational Biology/Bioinformatics.
- Control, Robotics, Mechatronics.
- Theory of Computation.
- Local Subjects:
- Artificial Intelligence.
- Computational Intelligence.
- Optimization.
- Computational Biology/Bioinformatics.
- Control, Robotics, Mechatronics.
- Theory of Computation.
- Physical Description:
- 1 online resource (XIII, 123 pages) : 27 illustrations, 4 illustrations in color.
- Edition:
- First edition 2017.
- Contained In:
- Springer eBooks
- Place of Publication:
- Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2017.
- System Details:
- text file PDF
- Summary:
- 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.
- Contents:
- LCSs in a Nutshell
- LCS Concepts
- Functional Cycle Components
- LCS Adaptability
- Applying LCSs.
- Other Format:
- Printed edition:
- ISBN:
- 978-3-662-55007-6
- 9783662550076
- Access Restriction:
- Restricted for use by site license.
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