Optimisation of Mixed Polarity Reed-Muller Functions

Abstract

This paper presents a genetic algorithm (GA) search method in order to obtain better circuit implementation of the mixed polarity Reed-Muller functions. By combining global searching ability of genetic algorithm and local searching ability of simulated annealing, the proposed GA method could achieve fast convergence. It differs to traditional genetic algorithm, in which the proposed GA forms an intermediate population by using 2/3 population from previous generation and 2/3 population from current generation at the annealing stage. Annealing is then applied to the intermediate population to generate a new population. In the next generation selection, crossover and mutation operations are used for the newly generated population. The calculation of cost function of proposed algorithm is based on parallel tabular technique to overcome the disadvantage of the traditional tabular technique. The results of the tested benchmark indicated that this algorithm is highly effective for searching the best polarity and it could achieve 29% area reduction and 3.68X speedup.