Polarity Searching for MPRM Logic Circuit Based on Improved Adaptive Genetic Algorithm

In combinational logic circuits, expressing logic functions in terms of Mixed Polarity Reed-Muller (MPRM) expansions draws more and more attention for its advantages over Boolean logic and Fixed Polarity Reed-Muller (FPRM) expansions. For n-variable MPRM logic function, the polarity directly determines the expression form of the circuits, and then influences the power dissipation of the circuit. However, many literatures tend to research the optimization of the MPRM ignoring the polarity traversal sequence for large-scale circuits. This paper presents an Improved Adaptive Genetic Algorithm (IAGA) to optimize the best polarity traversal sequence of MPRM logic circuits to speed up the polarity optimization. The proposed algorithm has been carried out in C language, and a comparative analysis has been presented for MCNC benchmark circuits. The results show that this algorithm gives best polarity and does well in reducing the time of polarity searching.