  In this paper, we introduce a Model-based Algorithm Turning Engine, namely
MATE, where the parameters of an algorithm are represented as expressions of
the features of a target optimisation problem. In contrast to most static
(feature-independent) algorithm tuning engines such as irace and SPOT, our
approach aims to derive the best parameter configuration of a given algorithm
for a specific problem, exploiting the relationships between the algorithm
parameters and the features of the problem. We formulate the problem of finding
the relationships between the parameters and the problem features as a symbolic
regression problem and we use genetic programming to extract these expressions.
For the evaluation, we apply our approach to configuration of the (1+1) EA and
RLS algorithms for the OneMax, LeadingOnes, BinValue and Jump optimisation
problems, where the theoretically optimal algorithm parameters to the problems
are available as functions of the features of the problems. Our study shows
that the found relationships typically comply with known theoretical results,
thus demonstrating a new opportunity to consider model-based parameter tuning
as an effective alternative to the static algorithm tuning engines.
