Affiliations: Computer Engineering Department, Iran University of
Science and Technology, Teheran, Iran. E-mails: [email protected],
[email protected]
Note: [] Address for correspondence: Computer Engineering Department,
Iran University of Science and Technology, Narmak, Tehran, Iran
Abstract: Learning Classifier Systems are Evolutionary Learning mechanisms
which combine Genetic Algorithm and the Reinforcement Learning paradigm.
Learning Classifier Systems try to evolve state-action-reward mappings to
propose the best action for each environmental state to maximize the achieved
reward. In the first versions of learning classifier systems, state-action
pairs can only be mapped to a constant real-valued reward. So to model a fairly
complex environment, LCSs had to develop redundant state-action pairs which had
to be mapped to different reward values. But an extension to a well-known LCS,
called Accuracy Based Learning Classifier System or XCS, was recently developed
which was able to map state-action pairs to a linear reward function. This new
extension, called XCSF, can develop a more compact population than the original
XCS. But some further researches have shown that this new extension is not able
to develop proper mappings when the input parameters are from certain
intervals. As a solution to this issue, in our previous works, we proposed a
novel solution inspired by the idea of using evolutionary approach to
approximate the reward landscape. The first results seem promising, but our
approach, called XCSFG, converged to the goal very slowly. In this paper, we
propose a new extension to XCSFG which employs micro-GA which its needed
population is extremely smaller than simple GA. So we expect micro-GA to help
XCSFG to converge faster. Reported results show that this new extension can be
assumed as an alternative approach in XCSF family with respect to its
convergence speed, approximation accuracy and population compactness.
