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Paper Details

Paper Title
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 algorithms behavior and the operators that support the evolution of feasible solutions into optimized solutions.
Keyword
Genetic Algorithm, fitness function, cross over, mutation,
Others Details
Paper Id : 86576
Author Name : P.Muthulakshmi
Co-Author Name(s) : E.AarthiP.Yogalakshmi
Volume/Issue No : Volume 05 Issue 02
Page No : 63-67
DOI Number : DOI:10.21090/IJAERD.86576
License : This work is licensed under a Creative Commons Attribution 4.0 International License.
website : http://www.ijaerd.com/index.php
Impact Factor : 5.71, SJIF-2017
ISSN Details : eISSN: 2348-4470, pISSN:2348-6406