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
Functional approaches to generating orderings for efficient symbolic representations
DAC '92 Proceedings of the 29th ACM/IEEE Design Automation Conference
Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
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
Learning Heuristics for OBDD Minimization by Evolutionary Algorithms
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Multi-objected Optimization in Evolutionary Algorithms Using Satisfiability Classes
Proceedings of the 6th International Conference on Computational Intelligence, Theory and Applications: Fuzzy Days
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
Too Much Knowledge Hurts: Acceleration of Genetic Programs for Learning Heuristics
Proceedings of the International Conference, 7th Fuzzy Days on Computational Intelligence, Theory and Applications
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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.