Graph-Based Algorithms for Boolean Function Manipulation
IEEE Transactions on Computers
Distributed genetic algorithms
Proceedings of the third international conference on Genetic algorithms
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
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
The nonapproximability of OBDD minimization
Information and Computation
Metric Based Evolutionary Algorithms
Proceedings of the European Conference on Genetic Programming
Evolving binary decision diagrams with emergent variable orderings
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
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When genetic programming (GP) is used to find programs witli Boolean inputs and outputs, ordered binary decision diagrams (OBDDs) are often used successfully. In all known OBDD-based GP-systems the variable ordering, a crucial factor for the size of OBDDs, is preset to an optimal ordering of the known test function. Certainly this cannot be done in practical applications, where the function to learn and hence its optimal variable ordering are unknown. Here, the first GP-system is presented that evolves the variable ordering of the OBDDs and the OBDDs itself by using a distributed hybrid approach. For the experiments presented the unavoidable size increase compared to the optimal variable ordering is quite small. Hence, this approach is a big step towards learning well-generalizing Boolean functions.