Introduction to Learning Classifier Systems

  • Ryan J. Urbanowicz
  • Will N. Browne

Part of the SpringerBriefs in Intelligent Systems book series (BRIEFSINSY)

Table of contents

  1. Front Matter
    Pages i-xiii
  2. Ryan J. Urbanowicz, Will N. Browne
    Pages 1-19
  3. Ryan J. Urbanowicz, Will N. Browne
    Pages 21-40
  4. Ryan J. Urbanowicz, Will N. Browne
    Pages 41-70
  5. Ryan J. Urbanowicz, Will N. Browne
    Pages 71-102
  6. Ryan J. Urbanowicz, Will N. Browne
    Pages 103-123

About this book


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.


Learning Classifier System (LCS) Computational Intelligence Machine Learning (ML) Genetics-Based Machine Learning (GBML) Adaptive Systems Rule Discovery Evolutionary Computing (EC)

Authors and affiliations

  • Ryan J. Urbanowicz
    • 1
  • Will N. Browne
    • 2
  1. 1.Dept. for Biostatistics and EpidemiologyUniversity of PennsylvaniaPhiladelphiaUSA
  2. 2.School of Engineering & Computer ScienceVictoria University of WellingtonWellingtonNew Zealand

Bibliographic information