Paper Details

The study presents a pragmaticoutlook of Genetic Algorithms. Many biological algorithms are inspired by themechanisms applied on evolution and among these Genetic Algorithms are widely accepted as they well suit evolutionarycomputing models that generate optimal solutions on random as well as deterministic problems. Genetic Algorithms aremathematical approaches to imitate processes studied in natural evolution. The methodology of GA is intensivelyexperimented in order to use the power of evolution to solve optimization problems. Genetic Algorithms are adaptiveheuristic search algorithm based on the evolutionary ideas of genetics and natural selection. These algorithms exploitrandom search approach to solve optimization problems. Genetic Algorithms take benefits of historical information todirect the search into the convergence of better performance within the search space. The basic techniques of theevolutionary algorithm are observed to be the simulated processes in natural systems. Thesetechniques are aimed tocarry the effective population to the next generation and ensure the survival of the fittest. Nature supports the dominationof stronger over the weaker ones in any kind. In this study,we proposedthe arithmetic views of genetic algorithm’sbehavior and the operators that support the evolution of feasible solutions into optimized solutions.

P. Muthulakshmi, Aarthi E, P.Yogalakshmi, “Genetic Algorithm – An Empirical View”, International Journal of Advance Engineering and Research Development (IJAERD), Vol. 5, Issue 2, pp. 63-67, February 2018.