LCS Concepts

  • Ryan J. UrbanowiczEmail author
  • Will N. Browne
Part of the SpringerBriefs in Intelligent Systems book series (BRIEFSINSY)


This chapter aims to provide an appreciation for core concepts that separate LCSs from other techniques. In particular, we provide insight into why they work, and how they are conceptually unique. It is hoped that the reader will appreciate that LCSs represent a machine learning concept, rather than a single technique. We consider the answers to questions such as (1) Rules/classifiers - What are they? Plus, how can they be represented and evaluated? (2) Why do we evolve a population of rules rather than a single rule as a solution? (3) What is the importance of cooperation and competition among classifiers? (4) How does an LCS interact with problems to find and generalise useful patterns? (5) What problem properties should be considered when deciding whether to apply an LCS? and (6) What are the general advantages and disadvantages of LCSs? The functional cycle and how to begin implementing an LCS are covered in the next chapter.


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Copyright information

© The Author(s) 2017

Authors and Affiliations

  1. 1.Department for Biostatistics and EpidemiologyUniversity of PennsylvaniaPhiladelphiaUSA
  2. 2.School of Engineering & Computer ScienceVictoria University of WellingtonWellingtonNew Zealand

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