Sorting The Right Trajectory Using AI Coupling AI With Varied Sorting Algorithms to Identify The Best Fit Algorithm While Solving Real-World Challenges

There have been many sorting algorithms available, but it’s always been a challenge to find out which one is the right to be implemented to solve real-world challenges when the dataset is huge to give you the best and most optimal results. The objective of this paper is to find from among the three candidate algorithms – Quicksort, Insertion sort, and Counting sort, to find out which one outperforms the other dynamically by the use of a Machine Learning algorithm on real-time data set with varied volume and variations to help implement the algorithm which gives most optimal results.

Sorting algorithms, Machine Learning, Meta-learning, Algorithm Selection, Quicksort, Counting sort, Decision Trees, Gradient Boosting, Empirical Software Performance

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