Genetic Algorithm – A Sensible Evolutionary Optimization Technique
P Muthulakshmi1, E Aarthi2

1P Muthulakshmi, Department of Computer Science, SRM Institute of Science and Technology, Chennai, (Tamil Nadu), India-603 203.
2E Aarthi , Department of Computer Science, SRM Institute of Science and Technology, Chennai, (Tamil Nadu), India-603 203.

Manuscript received on 30 June 2019 | Revised Manuscript received on 09 July 2019 | Manuscript published on 30 July 2019 | PP: 2805-2809| Volume-8 Issue-9, July 2019 | Retrieval Number: I8651078919/19©BEIESP | DOI: 10.35940/ijitee.I8651.078919
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Abstract: 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.
Keywords: Genetic Algorithm, Fitness function, Cross over, Mutation, Optimization technique

Scope of the Article: Discrete Optimization