Abstract
Learning Classifier Systems (LCSs), such as XCS and other accuracy-based
classifier systems, evolve a distributed problem solution online.
During the learning process, rule quality is assessed iteratively
using
techniques based on gradient-descent,
while the rule structure is evolved using selection and variation
operators of evolutionary algorithms.
%Standard LCSs used simple recombination operators during rule evolution.
%One-point, two-point, or uniform crossover were used to recombine
classifier offspring.
While using standard variation operators suffices for solving some
problems,
it does not assure
an effective evolutionary search in many difficult problems that contain
strong interactions between features.
Specifically, it was shown that standard crossover operators can frequently
disrupt important combinations of features, which often results in
poor performance.
This chapter describes how advanced EDAs can be integrated into XCS
in order
to ensure effective exploration even for problems in which features
strongly
interact and standard variation operators lead to poor XCS performance.
In
particular, the chapter incorporates the model building and sampling
techniques
from BOA and ECGA into XCS. The chapter shows that the two
proposed algorithms ensure that the solution is found efficiently
and reliably. The results presented in this chapter thus suggest
that the research on combining standard LCSs with advanced EDAs holds
a big promise and represents an important area for future research
on LCSs and EDAs.
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