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Advances in Learning Classifier Systems

Overview of attention for book
Cover of 'Advances in Learning Classifier Systems'

Table of Contents

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    Book Overview
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    Chapter 1 Biasing Exploration in an Anticipatory Learning Classifier System
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    Chapter 2 An Incremental Multiplexer Problem and Its Uses in Classifier System Research
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    Chapter 3 A Minimal Model of Communication for a Multi-agent Classifier System
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    Chapter 4 A Representation for Accuracy-Based Assessment of Classifier System Prediction Performance
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    Chapter 5 A Self-Adaptive XCS
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    Chapter 6 Two Views of Classifier Systems
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    Chapter 7 Social Simulation Using a Multi-agent Model Based on Classifier Systems: The Emergence of Vacillating Behaviour in the “El Farol” Bar Problem
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    Chapter 8 Advances in Learning Classifier Systems
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    Chapter 9 A Preliminary Investigation of Modified XCS as a Generic Data Mining Tool
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    Chapter 10 Explorations in LCS Models of Stock Trading
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    Chapter 11 On-Line Approach for Loss Reduction in Electric Power Distribution Networks Using Learning Classifier Systems
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    Chapter 12 Compact Rulesets from XCSI
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    Chapter 13 An Algorithmic Description of ACS2
Attention for Chapter 2: An Incremental Multiplexer Problem and Its Uses in Classifier System Research
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Citations

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Chapter title
An Incremental Multiplexer Problem and Its Uses in Classifier System Research
Chapter number 2
Book title
Advances in Learning Classifier Systems
Published by
Springer Berlin Heidelberg, January 2002
DOI 10.1007/3-540-48104-4_2
Book ISBNs
978-3-54-043793-2, 978-3-54-048104-1
Authors

Lawrence Davis, Chunsheng Fu, Stewart W. Wilson

Editors

Pier Luca Lanzi, Wolfgang Stolzmann, Stewart W. Wilson

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 3 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Australia 1 33%
Unknown 2 67%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 67%
Professor 1 33%
Readers by discipline Count As %
Computer Science 3 100%

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