Optimization of Mixed Polarity Functions Using Genetic Algorithm with Parallel Tabular Technique

This paper presents a genetic algorithm(GA) using parallel tabular technique for the optimization of mixed polarity functions.The algorithm is to find optimal solution among 3n different solutions for large functions.To overcome the slow convergence of GA,the calculation of the cost function is based on parallel tabular technique,in which new on-set terms are generated at one time instead of generating in sequence.As a result,the correlation between newly generated terms and previously generated terms is avoided.Experimental results show that,the proposed algorithm is efficient in terms of CPU time and achieves 8% improvement on average,without generating all the possible 3n polarities.