Optimization of mixed polarity reed-muller functions using genetic algorithm

In this paper, genetic algorithm (GA) using parallel tabular technique is presented for the optimization of mixed polarity Reed Muller and mixed polarity dual Reed Muller functions. The algorithm is to find optimal solution among 3ⁿ different solutions for large functions. To overcome the disadvantage of the traditional tabular technique, the cost function of GA is based on parallel tabular technique, in which new terms are generated at one time instead of generating in sequence. Without generating all the polarities, the proposed algorithm is efficient in terms of CPU time and achieves 8% improvement in average.