  For many optimization problems it is possible to define a distance metric
between problem variables that correlates with the likelihood and strength of
interactions between the variables. For example, one may define a metric so
that the dependencies between variables that are closer to each other with
respect to the metric are expected to be stronger than the dependencies between
variables that are further apart. The purpose of this paper is to describe a
method that combines such a problem-specific distance metric with information
mined from probabilistic models obtained in previous runs of estimation of
distribution algorithms with the goal of solving future problem instances of
similar type with increased speed, accuracy and reliability. While the focus of
the paper is on additively decomposable problems and the hierarchical Bayesian
optimization algorithm, it should be straightforward to generalize the approach
to other model-directed optimization techniques and other problem classes.
Compared to other techniques for learning from experience put forward in the
past, the proposed technique is both more practical and more broadly
applicable.
