Genetic Algorithm – An Empirical View

Abstract

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