NDLI: Learning classifier systems: a gentle introduction

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Learning classifier systems: a gentle introduction

Content Provider	ACM Digital Library
Author	Lanzi, Pier Luca
Abstract	Learning Classifier Systems were introduced in the 1970s by John H. Holland as highly adaptive, cognitive systems. More than 40 years later, the introduction of Stewart W. Wilson's XCS, a highly engineered classifier system model, has transformed them into a state-of-the-art machine learning system. Learning classifier systems can effectively solve data-mining problems, reinforcement learning problems, and also cognitive, robotics control problems. In comparison to other, non-evolutionary machine learning techniques, their performance is competitive or superior, dependent on the setup and problem. Learning classifier systems can work both online and offline, they are extremely flexible, applicable to a larger range of problems, and are highly adaptive. Moreover, system knowledge can be easily extracted, visualized, or even used to focus the progressive search on particular interesting subspaces. This tutorial provides a gentle introduction to learning classifier systems and their general functionality. It then surveys the current theoretical understanding of the systems. Finally, we provide a suite of current successful LCS applications and discuss the most promising areas for future applications and research directions.
Starting Page	407
Ending Page	430
Page Count	24
File Format	PDF
ISBN	9781450328814
DOI	10.1145/2598394.2605343
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-07-12
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Genetics-based machine learning
Content Type	Text
Resource Type	Article

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