Open AccessGenetic Algorithm – A Sensible Evolutionary Optimization Technique
Author(s) -
P Muthulakshmi
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.i8651.078919
Subject(s) - cultural algorithm , genetic algorithm , quality control and genetic algorithms , computer science , evolutionary algorithm , population based incremental learning , genetic representation , meta optimization , survival of the fittest , mathematical optimization , heuristic , selection (genetic algorithm) , convergence (economics) , search algorithm , exploit , algorithm , population , mathematics , artificial intelligence , machine learning , demography , computer security , evolutionary biology , sociology , economic growth , economics , biology
The study presents a pragmatic outlook of genetic algorithm. Many biological algorithms are inspired for their ability to evolve towards best solutions and of all; genetic algorithm is widely accepted as they well suit evolutionary computing models. Genetic algorithm could generate optimal solutions on random as well as deterministic problems. Genetic algorithm is a mathematical approach to imitate the processes studied in natural evolution. The methodology of genetic algorithm is intensively experimented in order to use the power of evolution to solve optimization problems. Genetic algorithm is an adaptive heuristic search algorithm based on the evolutionary ideas of genetics and natural selection. Genetic algorithm exploits random search approach to solve optimization problems. Genetic algorithm takes benefits of historical information to direct the search into the convergence of better performance within the search space. The basic techniques of evolutionary algorithms are observed to be simulating the processes in natural systems. These techniques are aimed to carry 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 proposed the arithmetic views of the behavior and operators of genetic algorithm that support the evolution of feasible solutions to optimized solutions.