Genetic Algorithms for the Variable Ordering Problem of Binary Decision Diagrams

Wolfgang LendersWolfgang Lenders, Christel BaierChristel Baier

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Wolfgang Lenders, Christel Baier
Genetic Algorithms for the Variable Ordering Problem of Binary Decision Diagrams
Proc. of the 8th International Workshop on Foundations of Genetic Algorithms (FOGA), volume 3469 of Lecture Notes in Computer Science, 1--20, 2005. Springer

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KurzfassungAbstract
Ordered binary decision diagrams (BDDs) yield a data structure for switching functions that has been proven to be very useful in many areas of computer science. The major problem with BDD-based calculations is the variable ordering problem which addresses the question of finding an ordering of the input variables which minimizes the size of the BDD-representation. In this paper, we discuss the use of genetic algorithms to improve the variable ordering of a given BDD. First, we explain the main features of an implementation and report on experimental studies. In this context, we present a new crossover technique that turned out to be very useful in combination with sifting as hybridization technique. Second, we provide a definition of a distance graph which can serve as formal framework for efficient schemes for the fitness evaluation.
Forschungsgruppe:Research Group: Algebraische und logische Grundlagen der InformatikAlgebraic and Logical Foundations of Computer Science

The final publication is available at Springer via http://dx.doi.org/10.1007/11513575_1.

@inproceedings{LB2005,
  author    = {Wolfgang Lenders and Christel Baier},
  title     = {Genetic Algorithms for the Variable Ordering Problem of Binary
               Decision Diagrams},
  booktitle = {Proc. of the 8th International Workshop on Foundations of Genetic
               Algorithms (FOGA)},
  series    = {Lecture Notes in Computer Science},
  volume    = {3469},
  publisher = {Springer},
  year      = {2005},
  pages     = {1--20},
  doi       = {10.1007/11513575_1}
}