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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 11: On-Line Approach for Loss Reduction in Electric Power Distribution Networks Using Learning Classifier Systems
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Citations

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Chapter title
On-Line Approach for Loss Reduction in Electric Power Distribution Networks Using Learning Classifier Systems
Chapter number 11
Book title
Advances in Learning Classifier Systems
Published by
Springer Berlin Heidelberg, January 2002
DOI 10.1007/3-540-48104-4_11
Book ISBNs
978-3-54-043793-2, 978-3-54-048104-1
Authors

Patrícia Amâncio Vargas, Christiano Lyra Filho, Fernando J. Von Zuben

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 2 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 2 100%

Demographic breakdown

Readers by professional status Count As %
Professor 1 50%
Researcher 1 50%
Readers by discipline Count As %
Computer Science 1 50%
Energy 1 50%

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