by Alberto Franzin, Leslie Pérez Cáceres, and Thomas Stützle
2017
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In this work, we study the impact of altering the sampling space of
parameters in automatic algorithm configurators. We show that a
proper transformation can strongly improve the convergence towards
better configurations; at the same time, biases about
good parameter values, possibly based on misleading prior knowledge,
may lead to wrong choices in the transformations and be detrimental
for the configuration process. To emphasize the impact of the transformations,
we initially study their effect on configuration tasks
with a single parameter in different experimental settings. We also
propose a mechanism of how to adapt the transformation used and give
exemplary experimental results with that scheme.
We also propose a mechanism for how to adapt towards an appropriate
transformation and give exemplary experimental results of that scheme.
Keywords: automatic algorithm configuration, parameter value, transformation