Novel Reduction Methods for Decision Diagrams

We propose a novel method of reduction for binary-based decision diagrams (DD) exploiting the similarities between Boolean functions. Conventional methods are able to remove redundant parts DD that adhere (or represent) identical structures. The proposed approach tries take advantage not only but also equivalent diagram, which produce results after change in variable ordering. Basic types DD, like Binary (BDD) or Functional (FDD) do allow diagram as notation ordering would be lost. Therefore, we new type incorporate different orders avariables and thus allowing – Multi-Variable Decision Diagram (MVDD). remaining obstacle is determination equivalency, heuristic. paper presents Kronecker (KFDD) MVDD with regard multiple parameters, where optimization process relies heavily on evolutionary algorithms Residual Variable (RV). average added value size was 26.13% when compared BDD 12.27% KFDD while preserving similar values power consumption path length diagram.