USING GENETIC ALGORITHMS FOR THE VARIABLE ORDERING OF REED-MULLER BINARY DECISION DIAGRAMS

Results are reported of the use of genetic algorithms for the variable ordering problem in Reed-Muller binary decision diagrams. Tests carri ed out on benchmark examples and randomly generated functions are very encouraging and compare favourably with other non-exhaustive algorith ms. The results show significant reduction in the number of nodes and gates for multi-level designs. This work is confined to single output fixed polarity Reed-Muller expansions: the method will be developed fu rther to extend to other forms and to multi-outputs.