Two Views of Classifier Systems

Tim Kovacs; Lanzi P. L.; Stolzmann W.; Wilson S. W.

Two Views of Classifier Systems

Tim Kovacs
, Lanzi P. L.
, Stolzmann W.
, Wilson S. W.

Research output: Chapter in Book/Report/Conference proceeding › Chapter in a book

6 Citations (Scopus)

Abstract

This work suggests two ways of looking at Michigan classifier systems; as Genetic Algorithm-based systems, and as Reinforcement Learning-based systems, and argues that the former is more suitable for traditional strength-based systems while the latter is more suitable for accuracy-based XCS. The dissociation of the Genetic Algorithm from policy determination in XCS is noted, and the two types of Michigan classifier system are contrasted with Pittsburgh systems.

Translated title of the contribution	Two Views of Classifier Systems
Original language	English
Title of host publication	Advances in Learning Classifier Systems
Publisher	Springer
Volume	2321
ISBN (Print)	3540437932
Publication status	Published - 2002

Bibliographical note

Other page information: 74-87
Other identifier: 1000647

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title = "Two Views of Classifier Systems",

abstract = "This work suggests two ways of looking at Michigan classifier systems; as Genetic Algorithm-based systems, and as Reinforcement Learning-based systems, and argues that the former is more suitable for traditional strength-based systems while the latter is more suitable for accuracy-based XCS. The dissociation of the Genetic Algorithm from policy determination in XCS is noted, and the two types of Michigan classifier system are contrasted with Pittsburgh systems.",

author = "Tim Kovacs and \{P. L.\}, Lanzi and Stolzmann W. and \{S. W.\}, Wilson",

note = "Other page information: 74-87 Other identifier: 1000647",

year = "2002",

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T1 - Two Views of Classifier Systems

AU - Kovacs, Tim

AU - P. L., Lanzi

AU - W., Stolzmann

AU - S. W., Wilson

N1 - Other page information: 74-87 Other identifier: 1000647

PY - 2002

Y1 - 2002

N2 - This work suggests two ways of looking at Michigan classifier systems; as Genetic Algorithm-based systems, and as Reinforcement Learning-based systems, and argues that the former is more suitable for traditional strength-based systems while the latter is more suitable for accuracy-based XCS. The dissociation of the Genetic Algorithm from policy determination in XCS is noted, and the two types of Michigan classifier system are contrasted with Pittsburgh systems.

AB - This work suggests two ways of looking at Michigan classifier systems; as Genetic Algorithm-based systems, and as Reinforcement Learning-based systems, and argues that the former is more suitable for traditional strength-based systems while the latter is more suitable for accuracy-based XCS. The dissociation of the Genetic Algorithm from policy determination in XCS is noted, and the two types of Michigan classifier system are contrasted with Pittsburgh systems.

M3 - Chapter in a book

SN - 3540437932

VL - 2321

BT - Advances in Learning Classifier Systems

PB - Springer

ER -

Two Views of Classifier Systems

Abstract

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