ABSTRACT
Learn-and-Optimize (LaO) is a generic surrogate based
method for parameter tuning combining learning and optimization.
In this paper LaO is used to tune Divide-and-
Evolve (DaE), an Evolutionary Algorithm for AI Planning.
The LaO framework makes it possible to learn the relation
between some features describing a given instance and
the optimal parameters for this instance, thus it enables
to extrapolate this knowledge to unknown instances in the
same domain. Moreover, the learned relation is used as a
surrogate-model to accelerate the search for the optimal parameters.
It hence becomes possible to solve intra-domain
and extra-domain generalization in a single framework. The
proposed implementation of LaO uses an Articial Neural
Network for learning the mapping between features and optimal
parameters, and the Covariance Matrix Adaptation
Evolution Strategy for optimization. Results demonstrate
that LaO is capable of improving the quality of the DaE results
even with only a few iterations. The main limitation of
the DaE case-study is the limited amount of meaningful features
that are available to describe the instances. However,
the learned model reaches almost the same performance on
the test instances, which means that it is capable of generalization.
Categories and Subject Descriptors
I.2.6 [Computing Methodologies]: Articial Intelligence
Learning Parameter learning
General Terms
Theory
Keywords
parameter tuning, AI Planning, evolutionary algorithms
1. INTRODUCTION
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GECCO’11, July 12–16, 2011, Dublin, Ireland.
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