Parallelizing Mutations for Genetic Algorithm
Abstract
In the present work, the author has implemented the mutations crossovers of genetic algorithm (GA) over a parallelism environment. This attempt will enable us collect data to build a framework for shared parallelism for the solution of equations in the same verifiable computational time. The data indicate towards the better implementation of a larger computational problem in the parallelized for the better solution in the verifiable execution time.
Keywords: Genetic algorithm, parallelism, time complexity
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