Abstract
The use and benefits of self-adaptive mutation operators are well
known within evolutionary computing. In this paper, we begin by examining
the use of self-adaptive mutation in learning classifier systems
with the aim of improving their performance as controllers for autonomous
mobile robots. We implement the operator in a zeroth level classifier
system, and examine its performance in two animat environments. It
is shown that although no significant increase in performance is
seen over results presented in the literature using a fixed rate
of mutation, the operator adapts to an appropriate rateregadless
of the initial range. The same concept is then applied to the learning
rate parameter, but results show that a modification must be made
to produce stable/effective controllers. Finally, results from a
fully self-adaptive system are presented, with marked benefits being
found in a nonstationary environment.
Users
Please
log in to take part in the discussion (add own reviews or comments).