Minimization of Digital Combinational Circuit using Genetic programming with modified fitness function
Peeyush Sharma, Trailokya Nath Sasamal · 2016
Evolutionary Multi-objective optimization using Genetic Algorithms (GA) are proven more powerful and efficient methods for optimization of complex digital circuit problems. In this paper, Genetic programming (GP) has been used based on GA to automate the design of the Digital Combinational Circuit. It is desired to minimize the total number of gates used and number of generations for evolved circuit. GP helps in evaluating the fitness for the circuits that is being evolved by GA. Here, evaluation is performed for the best fitness, average fitness and least fitness with the use of different gates used in GP. Results show, with the use of new constraint evaluation function and fitness function leads to improvement in number of generation, elapsed time and minimization in number of gates used in designing.