Graph-Based Algorithms for Boolean Function Manipulation
IEEE Transactions on Computers
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Who are the variables in your neighborhood
ICCAD '95 Proceedings of the 1995 IEEE/ACM international conference on Computer-aided design
Learning heuristics by genetic algorithms
ASP-DAC '95 Proceedings of the 1995 Asia and South Pacific Design Automation Conference
Improving the Variable Ordering of OBDDs Is NP-Complete
IEEE Transactions on Computers
Dynamic variable ordering for ordered binary decision diagrams
ICCAD '93 Proceedings of the 1993 IEEE/ACM international conference on Computer-aided design
Evolutionary algorithms for VLSI CAD
Evolutionary algorithms for VLSI CAD
Genetic Algorithms Plus Data Structures Equals Evolution Programs
Genetic Algorithms Plus Data Structures Equals Evolution Programs
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Heuristic Learning Based on Genetic Programming
EuroGP '01 Proceedings of the 4th European Conference on Genetic Programming
On variable ordering of binary decision diagrams for the application of multi-level logic synthesis
EURO-DAC '91 Proceedings of the conference on European design automation
Heuristic Learning Based on Genetic Programming
Genetic Programming and Evolvable Machines
Combining Decision Trees and Neural Networks for Drug Discovery
EuroGP '02 Proceedings of the 5th European Conference on Genetic Programming
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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.