Finding compact BDDs using genetic programming

Authors:
Ulrich Kühne;Nicole Drechsler
Affiliations:
Institute of Computer Science, University of Bremen, Bremen, Germany;Institute of Computer Science, University of Bremen, Bremen, Germany
Venue:
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
Year:
2006

Citing 10
Cited 0

Graph-Based Algorithms for Boolean Function Manipulation

IEEE Transactions on Computers
Two lower bounds for branching programs

STOC '86 Proceedings of the eighteenth annual ACM symposium on Theory of computing
Finding the optimal variable ordering for binary decision diagrams

DAC '87 Proceedings of the 24th ACM/IEEE Design Automation Conference
On the Complexity of VLSI Implementations and Graph Representations of Boolean Functions with Application to Integer Multiplication

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
Improving the Variable Ordering of OBDDs Is NP-Complete

IEEE Transactions on Computers
Evolution of Binary Decision Diagrams for Digital Circuit Design Using Genetic Programming

ICES '96 Proceedings of the First International Conference on Evolvable Systems: From Biology to Hardware
Evolving Objects: A General Purpose Evolutionary Computation Library

Selected Papers from the 5th European Conference on Artificial Evolution
Towards One-Pass Synthesis

Towards One-Pass Synthesis
BDDs-design, analysis, complexity, and applications

Discrete Applied Mathematics

Quantified Score

Hi-index	0.00

Visualization

Abstract

Binary Decision Diagrams (BDDs) can be used to design multiplexor based circuits. Unfortunately, the most commonly used kind of BDDs – ordered BDDs – has exponential size in the number of variables for many functions. In some cases, more general forms of BDDs are more compact. In constrast to the minimization of OBDDs, which is well understood, there are no heuristics for the construction of compact BDDs up to today. In this paper we show that compact BDDs can be constructed using Genetic Programming.