Evolving optimum populations with XCS classifier systems
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @Article{Iqbal:2013:SC,
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author = "Muhammad Iqbal and Will N. Browne and Mengjie Zhang",
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title = "Evolving optimum populations with XCS classifier
systems",
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journal = "Soft Computing",
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year = "2013",
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volume = "17",
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number = "3",
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pages = "503--518",
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month = mar,
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming, Learning
classifier systems, XCS, Optimal populations,
Scalability, Code fragments, Action consistency",
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ISSN = "1432-7643",
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DOI = "doi:10.1007/s00500-012-0922-5",
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language = "English",
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size = "16 pages",
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abstract = "The main goal of the research xdirection is to extract
building blocks of knowledge from a problem domain.
Once extracted successfully, these building blocks are
to be used in learning more complex problems of the
domain, in an effort to produce a scalable learning
classifier system (LCS). However, whilst current LCS
(and other evolutionary computation techniques)
discover good rules, they also create sub-optimum
rules. Therefore, it is difficult to separate good
building blocks of information from others without
extensive post-processing. In order to provide richness
in the LCS alphabet, code fragments similar to tree
expressions in genetic programming are adopted. The
accuracy-based XCS concept is used as it aims to
produce maximally general and accurate classifiers,
albeit the rule base requires condensation (compaction)
to remove spurious classifiers. Serendipitously, this
work on scalability of LCS produces compact rule sets
that can be easily converted to the optimum population.
The main contribution of this work is the ability to
clearly separate the optimum rules from others without
the need for expensive post-processing for the first
time in LCS. This paper identifies that consistency of
action in rich alphabets guides LCS to optimum rule
sets.",
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notes = "XCS with code fragmented action",
- }
Genetic Programming entries for
Muhammad Iqbal
Will N Browne
Mengjie Zhang
Citations