Overview

This carefully edited book brings together a fascinating selection of applications of Learning Classifier Systems (LCS). The book demonstrates the utility of this machine learning technique in recent real-world applications in such domains as data mining, modelling and optimization, and control. It shows how the LCS technique combines and exploits many Soft Computing approaches into a single coherent framework to produce an improved performance over other approaches.

Full Product Details

Author:   Larry Bull
Publisher:   Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
Imprint:   Springer-Verlag Berlin and Heidelberg GmbH & Co. K
Edition:   2004 ed.
Volume:   150
Dimensions:   Width: 15.50cm , Height: 1.90cm , Length: 23.50cm
Weight:   1.390kg
ISBN:  

9783540211099

ISBN 10:   3540211098
Pages:   305
Publication Date:   16 April 2004
Audience:   College/higher education ,  Professional and scholarly ,  Undergraduate ,  Postgraduate, Research & Scholarly
Format:   Hardback
Publisher's Status:   Active
Availability:   In Print  
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Table of Contents

Learning Classifier Systems: A Brief Introduction.- Section 1 — Data Mining.- Data Mining using Learning Classifier Systems.- NXCS Experts for Financial Time Series Forecasting.- Encouraging Compact Rulesets from XCS for Enhanced Data Mining.- Section 2 — Modelling and Optimization.- The Fighter Aircraft LCS: A Real-World, Machine Innovation Application.- Traffic Balance using Learning Classifier Systems in an Agent-based Simulation.- A Multi-Agent Model of the UK Market in Electricity Generation.- Exploring Organizational-Learning Oriented Classifier Systems in Real-World Problems.- Section 3 — Control.- Distributed Routing in Communication Networks using the Temporal Fuzzy Classifier System — a Study on Evolutionary Multi-Agent Control.- The Development of an Industrial Learning Classifier System for Data-Mining in a Steel Hop Strip Mill.- Application of Learning Classifier Systems to the On-Line Reconfiguration of Electric Power Distribution Networks.- Towards Distributed Adaptive Control for Road Traffic Junction Signals using Learning Classifier Systems.- Bibliography of Real-World Classifier Systems Applications.

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