- author = "Frank Schmiedle and Daniel Grosse and Rolf Drechsler and Bernd Becker",
- title = "Too Much Knowledge Hurts: Acceleration of Genetic Programs for Learning Heuristics",
- booktitle = "Computational Intelligence : Theory and Applications",
- year = "2001",
- editor = "Bernd Reusch",
- volume = "2206",
- series = "LNCS",
- pages = "479--491",
- address = "Dortmund, Germany",
- month = "1-3 " # oct,
- organisation = "7th Fuzzy Days",
- publisher = "Springer-Verlag",
- keywords = "genetic algorithms, genetic programming",
-
URL = "
http://ira.informatik.uni-freiburg.de/papers/2001/SGDB_2001.ps.gz",
- ISBN = "3-540-42732-5",
- size = "pages",
-
abstract = "Among many other applications, evolutionary methods
have been used to develop heuristics for several
optimization problems in VLSI CAD in recent years.
Although learning is performed according to a set of
training benchmarks, it is most important to generate
heuristics that have a good generalization behaviour
and hence are well suited to be applied to unknown
examples. Besides large runtimes for learning, the
major drawback of these approaches is that they are
very sensitive to a variety of parameters for the
learning process.
In this paper, we study the impact of different parameters, like e.g. stopping conditions, on the quality of the results for learning heuristics for BDD minimization. If learning takes too long, the developed heuristics become too specific for the set of training examples and in that case results of application to unknown problem instances deteriorate. It will be demonstrated here that runtime can be saved while even improving the generalization behaviour of the heuristics.",
- notes = "http://ls1-www.cs.uni-dortmund.de/fd7/",
