Learning heuristics for OKFDD minimization by evolutionary algorithms

Ordered Kronecker Functional Decision Diagrams (OKFDDs) are a data structure for efficient representation and manipulation of Boolean functions. OKFDDs are very sensitive to the chosen variable ordering and the decomposition type list, i.e. the size may vary from linear to exponential. In this paper we present an Evolutionary Algorithm (EA) that learns good heuristics for OKFDD minimization starting from a given set of basic operations. The difference to other previous approaches to OKFDD minimization is that the EA does not solve the problem directly. Rather, it develops strategies for solving the problem. To demonstrate the efficiency of our approach experimental results are given. The newly developed heuristics combine high quality results with reasonable time overhead