Ebook: Rule-Based Evolutionary Online Learning Systems: A Principled Approach to LCS Analysis and Design
Author: Dr. Martin V. Butz (auth.)
- Tags: Appl.Mathematics/Computational Methods of Engineering, Artificial Intelligence (incl. Robotics), Neurosciences, Applications of Mathematics
- Series: Studies in Fuzziness and Soft Computing 191
- Year: 2006
- Publisher: Springer-Verlag Berlin Heidelberg
- Edition: 1
- Language: English
- pdf
This book offers a comprehensive introduction to learning classifier systems (LCS) – or more generally, rule-based evolutionary online learning systems. LCSs learn interactively – much like a neural network – but with an increased adaptivity and flexibility. This book provides the necessary background knowledge on problem types, genetic algorithms, and reinforcement learning as well as a principled, modular analysis approach to understand, analyze, and design LCSs. The analysis is exemplarily carried through on the XCS classifier system – the currently most prominent system in LCS research. Several enhancements are introduced to XCS and evaluated. An application suite is provided including classification, reinforcement learning and data-mining problems. Reconsidering John Holland’s original vision, the book finally discusses the current potentials of LCSs for successful applications in cognitive science and related areas.
This book offers a comprehensive introduction to learning classifier systems (LCS) – or more generally, rule-based evolutionary online learning systems. LCSs learn interactively – much like a neural network – but with an increased adaptivity and flexibility. This book provides the necessary background knowledge on problem types, genetic algorithms, and reinforcement learning as well as a principled, modular analysis approach to understand, analyze, and design LCSs. The analysis is exemplarily carried through on the XCS classifier system – the currently most prominent system in LCS research. Several enhancements are introduced to XCS and evaluated. An application suite is provided including classification, reinforcement learning and data-mining problems. Reconsidering John Holland’s original vision, the book finally discusses the current potentials of LCSs for successful applications in cognitive science and related areas.
This book offers a comprehensive introduction to learning classifier systems (LCS) – or more generally, rule-based evolutionary online learning systems. LCSs learn interactively – much like a neural network – but with an increased adaptivity and flexibility. This book provides the necessary background knowledge on problem types, genetic algorithms, and reinforcement learning as well as a principled, modular analysis approach to understand, analyze, and design LCSs. The analysis is exemplarily carried through on the XCS classifier system – the currently most prominent system in LCS research. Several enhancements are introduced to XCS and evaluated. An application suite is provided including classification, reinforcement learning and data-mining problems. Reconsidering John Holland’s original vision, the book finally discusses the current potentials of LCSs for successful applications in cognitive science and related areas.
Content:
Front Matter....Pages I-XXI
Introduction....Pages 1-7
Prerequisites....Pages 9-30
Simple Learning Classifier Systems....Pages 31-50
The XCS Classifier System....Pages 51-64
How XCS Works: Ensuring Effective Evolutionary Pressures....Pages 65-90
When XCS Works: Towards Computational Complexity....Pages 91-122
Effective XCS Search: Building Block Processing....Pages 123-146
XCS in Binary Classification Problems....Pages 147-156
XCS in Multi-Valued Problems....Pages 157-179
XCS in Reinforcement Learning Problems....Pages 181-195
Facetwise LCS Design....Pages 197-206
Towards Cognitive Learning Classifier Systems....Pages 207-217
Summary and Conclusions....Pages 219-225
Back Matter....Pages 227-266
This book offers a comprehensive introduction to learning classifier systems (LCS) – or more generally, rule-based evolutionary online learning systems. LCSs learn interactively – much like a neural network – but with an increased adaptivity and flexibility. This book provides the necessary background knowledge on problem types, genetic algorithms, and reinforcement learning as well as a principled, modular analysis approach to understand, analyze, and design LCSs. The analysis is exemplarily carried through on the XCS classifier system – the currently most prominent system in LCS research. Several enhancements are introduced to XCS and evaluated. An application suite is provided including classification, reinforcement learning and data-mining problems. Reconsidering John Holland’s original vision, the book finally discusses the current potentials of LCSs for successful applications in cognitive science and related areas.
Content:
Front Matter....Pages I-XXI
Introduction....Pages 1-7
Prerequisites....Pages 9-30
Simple Learning Classifier Systems....Pages 31-50
The XCS Classifier System....Pages 51-64
How XCS Works: Ensuring Effective Evolutionary Pressures....Pages 65-90
When XCS Works: Towards Computational Complexity....Pages 91-122
Effective XCS Search: Building Block Processing....Pages 123-146
XCS in Binary Classification Problems....Pages 147-156
XCS in Multi-Valued Problems....Pages 157-179
XCS in Reinforcement Learning Problems....Pages 181-195
Facetwise LCS Design....Pages 197-206
Towards Cognitive Learning Classifier Systems....Pages 207-217
Summary and Conclusions....Pages 219-225
Back Matter....Pages 227-266
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