Parul Aggrawal^1,, Faisal Naved¹, Mohd Haider¹

Genetic Algorithm, an Artificial Intelligence approach is based on the theory of natural selection and evolution. Traditional methods of sorting data are too slow in finding an efficient solution when the input data is too large. In contrast, Genetic Algorithm generates fittest solutions to a problem by exploiting new regions in the search space. This paper targets the three most commonly used Bubble, Selection and Insertion sorting techniques and executes memory on an input ranging from 1,000 to 10,000 where the input is entered in increasing, decreasing and random order. It mainly uses the Genetic Algorithm approach to optimize the effect of the three algorithms by generating an output which is consistent in terms of time variations which is not otherwise. This has been achieved by exploiting the property of Genetic Algorithm by choosing best parameter for population size, encoding, selection criteria, operator choice and optimized fitness function.

genetic algorithm, sorting, selection, crossover, mutation