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. minimization 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.
Similar content being viewed by others
References
B. Bollig, M. Löbbing, and I. Wegener, “Simulated annealing to improve variable orderings for OBDDs,” in Int'l Workshop on Logic Synth., pp. 5b:5.1–5.10, 1995.
B. Bollig and I. Wegener, “Improving the variable ordering of OBDDs is NP-complete,” IEEE Trans. on Comp., vol. 45, no. 9, pp. 993–1002, 1996.
R. E. Bryant, “Graph-based algorithms for Boolean function manipulation,” IEEE Trans. on Comp., vol. 35, no. 8, pp. 677–691, 1986.
T. H. Cormen, C. E. Leierson, and R. C. Rivest, Introduction to Algorithms, MIT Press, McGraw-Hill Book Company, 1990.
L. Davis, Handbook of Genetic Algorithms, van Nostrand Reinhold: New York, 1991.
N. Drechsler, Ñber die Anwendung Evolution¨arer Algorithmen im Schaltkreisentwurf, Ph.D. thesis, Albert-Ludwigs-Universität, Freiburg, 2000.
N. Drechsler, R. Drechsler, and B. Becker, “Anew model for multi-objective optimization in evolutionary algorithms,” in Int'l Conference on Computational Intelligence (Fuzzy Days), B. Reusch (ed.), vol. 1625 of LNCS, Springer Verlag, Dortmund, Germany, 1999, pp. 108–117.
N. Drechsler, F. Schmiedle, D. Groβe, and R. Drechsler, “Heuristic learning based on genetic programming,” in Euro GP, J. Miller, M. Tomassini, P. M. Lanzi, C. Ryan, A. G. B. Tettamanzi, and W. B. Langdon (eds.), vol. 2038 of LNCS, Springer Verlag, Como, Italy, 2001, pp. 1–10.
R. Drechsler, Evolutionary Algorithms for VLSI CAD, Kluwer Academic Publishers, Dortrecht, The Netherlands, 1998.
R. Drechsler and B. Becker, “Learning heuristics by genetic algorithms,” in ASP Design Automation Conf., Mukuhari, Japan, 1995, pp. 349–352.
R. Drechsler, B. Becker, and N. Göckel, “Agenetic algorithm for minimization of fixed polarity Reed-Muller expressions,” in Int'l Conf. on Artificial Neural Networks and Genetic Algorithms, D. W. Pearson, N. C. Steele, and R. F. Albrecht (eds.), 1995, pp. 392–395.
R. Drechsler, N. Göckel, and B. Becker, “Learning heuristics for OBDD minimization by evolutionary algorithms,” in Parallel Problem Solving from Nature, H.-M. Voigt, W. Ebeling, I. Rechenberg, and H.-P. Schwefel (eds.), vol. 1141 of LNCS, Springer Verlag, Berlin, Germany, 1996, pp. 730–739.
H. Esbensen and E. S. Kuh, “EXPLORER: an interactive floorplaner for design space exploration,” in European Design Automation Conf., Geneva, Switzerland, 1996, pp. 356–361.
M. Fujita, Y. Matsunaga, and T. Kakuda, “On variable ordering of binary decision diagrams for the application of multi-level synthesis,” in European Conf. on Design Automation, 1991, pp. 50–54.
D. E. Goldberg, Genetic Algorithms in Search, Optimization & Machine Learning, Addision-Wesley Publishing Company, Inc., Boston, USA, 1989.
J. Horn, N. Nafpliotis, and D. E. Goldberg, “Aniched pareto genetic algorithm for multiobjective optimization,” in Int'l Conference on Evolutionary Computation, Orlando, USA, 1994.
J. Koza, Genetic Programming—On the Programming of Computers by means of Natural Selection, MIT Press, Boston, USA, 1992.
J. Koza, Genetic Programming II—Automatic Discovery of Reusable Programs, MIT Press, Boston, USA, 1994.
M. R. Mercer, R. Kapur, and D. E. Ross, “Functional approaches to generating orderings for efficient symbolic representations,” in Design Automation Conf., Anaheim, USA, 1992, pp. 624–627.
Z. Michalewicz, Genetic Algorithms + Data Structures = Evolution Programs, Springer-Verlag, Heidelberg, Germany, 1994.
S. Panda and F. Somenzi, “Who are the variables in your neighborhood,” in Int'l Workshop on Logic Synth., Lake Tahoe, USA, 1995, pp. 5b:5.11–5.20.
R. Rudell, “Dynamic variable ordering for ordered binary decision diagrams,” in Int'l Conf. on CAD, Santa Clara, USA, 1993, pp. 42–47.
F. Schmiedle, N. Drechsler, D. Groβe, and Drechsler, “Priorities in multi-objective optimization for genetic programming,” in Genetic and Evolutionary Computation Conference,” L. Spector, E. D. Goodman, A. Wu, W. B. Langdon, H.-M. Voigt, M. Gen, S. Sen, M. Dorigo, S. Pezeshk, M. H. Garzon, and E. Burke (eds.), San Francisco, USA, 2001, pp. 129–136.
F. Schmiedle, D. Groβe, R. Drechsler, and B. Becker, “Too much knowledge hurts: Acceleration of genetic programs for learning heuristics,” in Int'l Conference on Computational Intelligence (Fuzzy Days), B. Reusch (ed.), vol. 2206 of LNCS, Dortmund, Germany, 2001, pp. 479–491.
N. Srinivas and K. Deb, “Multiobjective optimization using nondominated sorting in genetic algorithms,” Evolutionary Computation, vol. 2, no. 3, pp. 221–248, 1995.
S. Yang, “Logic synthesis and optimization benchmarks user guide,” Technical Report 1/95, Microelectronic Center of North Carolina, 1991.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Schmiedle, F., Drechsler, N., Große, D. et al. Heuristic Learning Based on Genetic Programming. Genetic Programming and Evolvable Machines 3, 363–388 (2002). https://doi.org/10.1023/A:1020988925923
Issue Date:
DOI: https://doi.org/10.1023/A:1020988925923