Learning complex, overlapping and niche imbalance Boolean problems using XCS-based classifier systems
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- @Article{Iqbal:2013:EI,
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author = "Muhammad Iqbal and Will N. Browne and Mengjie Zhang",
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title = "Learning complex, overlapping and niche imbalance
{Boolean} problems using {XCS-based} classifier
systems",
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journal = "Evolutionary Intelligence",
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year = "2013",
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volume = "6",
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number = "2",
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pages = "73--91",
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keywords = "genetic algorithms, genetic programming, Learning
classifier systems, XCS, XCSCFA, Code fragments,
Overlapping problems, Niche imbalance",
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ISSN = "1864-5909",
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publisher = "Springer",
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URL = "http://dx.doi.org/10.1007/s12065-013-0091-1",
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DOI = "doi:10.1007/s12065-013-0091-1",
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size = "19 pages",
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abstract = "XCS is an accuracy-based learning classifier system,
which has been successfully applied to learn various
classification and function approximation problems.
Recently, it has been reported that XCS cannot learn
overlapping and niche imbalance problems using the
typical experimental setup. This paper describes two
approaches to learn these complex problems: firstly,
tune the parameters and adjust the methods of standard
XCS specifically for such problems. Secondly, apply an
advanced variation of XCS. Specifically, we developed
previously an XCS with code-fragment actions, named
XCSCFA, which has a more flexible genetic programming
like encoding and explicit state-action mapping through
computed actions. This approach is examined and
compared with standard XCS on six complex Boolean
datasets, which include overlapping and niche imbalance
problems. The results indicate that to learn
overlapping and niche imbalance problems using XCS, it
is beneficial to either deactivate action set
subsumption or use a relatively high subsumption
threshold and a small error threshold. The XCSCFA
approach successfully solved the tested complex,
overlapping and niche imbalance problems without
parameter tuning, because of the rich alphabet,
inconsistent actions and especially the redundancy
provided by the code-fragment actions. The major
contribution of the work presented here is overcoming
the identified problem in the wide-spread XCS
technique.",
- }
Genetic Programming entries for
Muhammad Iqbal
Will N Browne
Mengjie Zhang
Citations