https://github.com/anilerc/experimental-analysis-sorting-algorithms

This project aims to conduct an experiment and evaluate the time complexity metrics of the following sorting algorithms for each different input characteristic.
https://github.com/anilerc/experimental-analysis-sorting-algorithms

Last synced: 7 days ago
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This project aims to conduct an experiment and evaluate the time complexity metrics of the following sorting algorithms for each different input characteristic.

• Host: GitHub
• URL: https://github.com/anilerc/experimental-analysis-sorting-algorithms
• Owner: anilerc
• Created: 2021-12-07T20:28:55.000Z (almost 3 years ago)
• Default Branch: main
• Last Pushed: 2023-11-04T23:00:13.000Z (about 1 year ago)
• Last Synced: 2023-11-05T00:18:47.632Z (about 1 year ago)
• Language: Java
• Size: 2.26 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# experimental-evaluation-sort-algos

This project aims to conduct an experiment and evaluate the time complexity metrics of the following sorting algorithms for each different input characteristic.

There are five different input characterisitics:

Sorted,

Reverse sorted,

Almost sorted,

Duplicate,

Random.

The following sorting techniques are coded by us before the experiment:

Insertion Sort

Binary Insertion Sort

Merge Sort

Quick Sort

Quick Sort with Median of 3 Optimization

Heap Sort

Counting Sort

Then, for each algorithm, data sets representing each input characteristic were run. 

Finally, their sorting performance and relative comparison for each combination were meticulously investigated by us in the project report.

PLEASE VISIT REPORT & EVALUATION.PDF file for further info.