Introduction to Learning Classifier Systems

by Ryan J Urbanowicz, Will N. Browne

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.

136 pages, Paperback

Published September 6, 2017

Reviews

[Richard · Rating 4 out of 5]

What can I say? If you're in need of a detailed introduction to Learning Classifier Systems, this is the book for you! :-)

A Learning Classifier System is a way to evolve IF...THEN rules. The book focusses on the UCS and XCS algorithms.