Abstract
In this paper we present an approach to learning
heuristics based on Genetic Programming (GP) which can
be applied to problems in the VLSI CAD area. GP is used
to develop a heuristic that is applied to the problem
instance instead of directly solving the problem by
application of GP. The GP-based heuristic learning
method is applied to one concrete field from the area
of VLSI CAD, i.e. minimisation of Binary Decision
Diagrams (BDDs). Experimental results are given in
order to demonstrate that the GP-based method leads to
high quality results that outperform previous methods
while the run-times of the resulting heuristics do not
increase. Furthermore, we show that by clever
adjustment of parameters, further improvements such as
the saving of about 50% of the run-time for the
learning phase can be achieved.
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