Abstract
In this paper we present an approach to learning heuristics based on Genetic Programming (GP). Instead of directly solving the problem by application of GP, GP is used to develop a heuristic that is applied to the problem instance. By this, the typical large runtimes of evolutionary methods have to be invested only once in the learning phase. The resulting heuristic is very fast. The technique is applied to a field from the area of VLSI CAD, i.e. minimization of Binary Decision Diagrams (BDDs). We chose this topic due to its high practical relevance and since it matches the criteria where our algorithm works best, i.e. large problem instances where standard evolutionary techniques cannot be applied due to their large runtimes. Our experiments show that we obtain high quality results that outperform previous methods, while keeping the advantage of low runtimes.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
R.E. Bryant. Graph-based algorithms for Boolean function manipulation. IEEE Trans. on Comp., 8:677–691, 1986.
B. Bollig and I. Wegener. Improving the variable ordering of OBDDs is NP-complete. IEEE Trans. on Comp., vol. 45,num. 9, pages 993–1002, 1996.
B. Bollig, M. Löbbing, and I. Wegener. Simulated annealing to improve variable orderings for OBDDs. In Int’l Workshop on Logic Synth., pages 5b:5.1–5.10, 1995.
R. Drechsler and B. Becker. Learning heuristics by genetic algorithms. In ASP Design Automation Conf., pages 349–352, 1995.
R. Drechsler and B. Becker. Binary Decision Diagrams—Theory and Implementation, Kluwer Academic Publishers, 1998
R. Drechsler, B. Becker, and N. Göckel. A genetic algorithm for variable ordering of OBDDs. In Int’l Workshop on Logic Synth., pages P5c:5.55–5.64, 1995.
N. Drechsler, R. Drechsler, and B. Becker. A new model for multi-objective optimization in evolutionary algorithms. In Fuzzy’99, LNCS 1625, pages 108–117, 1999.
R. Drechsler, N. Göckel, and B. Becker. Learning heuristics for OBDD minimization by evolutionary algorithms. In PPSN’96, LNCS 1141, pages 730–739, 1996.
M. Fujita, Y. Matsunga, and T. Kakuda. On variable ordering of binary decision diagrams for the application of multi-level synthesis. In European Conf. on Design Automation, pages 50–54, 1991.
J. Koza. Genetic Programming-On the Programming of Computers by means of Natural Selection. MIT Press, 1992
J. Koza. Genetic Programming II-Automatic Discovery of Reusable Programs. MIT Press, 1994
S. Panda and F. Somenzi. Who are the variables in your neighborhood. In Int’l Conf. on CAD, pages 74–77, 1995.
D.E. Ross, K.M. Butler, R. Kapur, and M.R. Mercer. Functional approaches to generating orderings for efficient symbolic representations. In Design Automation Conf., pages 624–627, 1992.
R. Rudell. Dynamic variable ordering for ordered binary decision diagrams. In Int’l Conf. on CAD, pages 42–47, 1993.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Drechsler, N., Schmiedle, F., Große, D., Drechsler, R. (2001). Heuristic Learning Based on Genetic Programming. In: Miller, J., Tomassini, M., Lanzi, P.L., Ryan, C., Tettamanzi, A.G.B., Langdon, W.B. (eds) Genetic Programming. EuroGP 2001. Lecture Notes in Computer Science, vol 2038. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45355-5_1
Download citation
DOI: https://doi.org/10.1007/3-540-45355-5_1
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-41899-3
Online ISBN: 978-3-540-45355-0
eBook Packages: Springer Book Archive