A Genetic Algorithm For Minimization Of Fixed Polarity Reed-Muller Expressions

A Genetic Algorithm (GA) is developed to find small or minimal Fixed Polarity Reed-Muller expressions (FPRMs) for large functions. We combine the GA with greedy heuristics, i.e. we use Hybrid GAs (HGAs). We show by experiments that results superior to all other approaches for large functions can be obtained using GAs. 1 Introduction Genetic Algorithms (GAs) are often used in optimization and machine learning [6, 2]. In many applications they are superior to the classical optimization techniques, e.g. gradient-descent. Recently, GAs have succesfully been applied to several hard problems in CAD [3]. The high complexity of modern VLSI circuitry has shown an increasing demand for synthesis tools. In the last few years synthesis based on AND/EXOR realizations has gained more and more interest, because AND/EXOR realizations are very efficient for large classes of circuits [9, 8]. In the following we consider a restricted class of EXOR Sum-Of-Products expressions (ESOPs), called Fixed Pol..