Improving genetic search in XCS-based classifier systems through understanding the evolvability of classifier rules
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- @Article{Iqbal:2015:SC,
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author = "Muhammad Iqbal and Will N. Browne and Mengjie Zhang",
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title = "Improving genetic search in XCS-based classifier
systems through understanding the evolvability of
classifier rules",
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journal = "Soft Computing",
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year = "2015",
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volume = "19",
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number = "7",
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pages = "1863--1880",
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month = jul,
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keywords = "genetic algorithms, genetic programming, Learning
classifier systems, XCS, XCSCFA, Evolvability",
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ISSN = "1432-7643",
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DOI = "doi:10.1007/s00500-014-1369-7",
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size = "18 pages",
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abstract = "Learning classifier systems (LCSs), an established
evolutionary computation technique, are over 30 years
old with much empirical testing and foundations of
theoretical understanding. XCS is a well-tested LCS
model that generates optimal (i.e., maximally general
and accurate) classifier rules in the final solution.
Previous work has hypothesised the evolution mechanisms
in XCS by identifying the bounds of learning and
population requirements. However, no work has shown
exactly how an optimum rule is evolved or especially
identifies whether the methods within an LCS are being
effectively. In this paper, we introduce a method to
trace the evolution of classifier rules generated in an
XCS-based classifier system. Specifically, we introduce
the concept of a family tree, termed parent-tree, for
each individual classifier rule generated in the system
during training, which describes the whole generational
process for that classifier. Experiments are conducted
on two sample Boolean problem domains, i.e.,
multiplexer and count ones problems using two XCS-based
systems, i.e., standard XCS and XCS with code-fragment
actions. The analysis of parent-trees reveals, for the
first time in XCS, that no matter how specific or
general the initial classifiers are, all the optimal
classifiers are converged through the mechanism be
specific then generalize near the final stages of
evolution. Populations where the initial classifiers
were slightly more specific than the known ideal
specificity in the target solutions evolve faster than
either very specific, ideal or more general starting
classifier populations. Consequently introducing the
flip mutation method and reverting the conventional
wisdom back to apply rule discovery in the match set
has demonstrated benefits in binary classification
problems, which has implications in using XCS for
knowledge discovery tasks. It is further concluded that
XCS does not directly all relevant information or all
breeding strategies to evolve the optimum solution,
indicating areas for performance and efficiency
improvement in XCS-based systems.",
- }
Genetic Programming entries for
Muhammad Iqbal
Will N Browne
Mengjie Zhang
Citations