https://github.com/dhou22/operational-research-project---dataset-synchronization-algorithms

This project investigates algorithms for synchronizing two identical datasets, comparing Greedy, Local Search, and Genetic approaches.
https://github.com/dhou22/operational-research-project---dataset-synchronization-algorithms

algorithms-implemented python

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This project investigates algorithms for synchronizing two identical datasets, comparing Greedy, Local Search, and Genetic approaches.

• Host: GitHub
• URL: https://github.com/dhou22/operational-research-project---dataset-synchronization-algorithms
• Owner: dhou22
• Created: 2024-12-17T18:03:41.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2025-01-22T12:37:35.000Z (over 1 year ago)
• Last Synced: 2025-01-22T13:37:21.940Z (over 1 year ago)
• Topics: algorithms-implemented, python
• Language: Jupyter Notebook
• Homepage:
• Size: 457 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Operational Research Project - Dataset Synchronization Algorithms

*******************************************************************

**Project Overview** 


- This project explores and evaluates the performance of various algorithms for dataset synchronization, including genetic algorithms, greedy algorithms, and local search algorithms. 


- The goal is to understand the strengths and weaknesses of each algorithm when applied to datasets of varying sizes, providing insights into their efficiency and scalability. 


- The findings from this analysis aim to guide the selection of the most suitable algorithm for specific dataset synchronization tasks. 
 


**Key Findings**

  - Genetic Algorithm: 


     - Demonstrates superior performance on small datasets. 


     - Struggles with larger datasets, often failing to deliver optimal results due to increased computational complexity. 


  - Greedy Algorithm: 


     - Excels in handling larger datasets. 


     - Consistently outperforms both the local search and genetic algorithms in scenarios involving larger data sizes. 


  - Local Search Algorithm: 


     - Performs moderately well but does not achieve the efficiency of the greedy algorithm on large datasets. 
 


   

 **Methodology** 


   - Problem Understanding: 


      - Analyzing the requirements and challenges associated with dataset synchronization.


    

  - Algorithm Implementation: 


     - Implementing genetic, greedy, and local search algorithms for comparative analysis. 


     

  - Dataset Analysis : 


     - Evaluating algorithm performance on datasets of varying sizes to identify patterns and trends. 


  - Performance Metrics : 


     - Assessing efficiency, scalability, and solution quality using appropriate metrics.. 
 

  - Insights and Documentation : 


     - Drawing actionable conclusions to guide algorithm selection in future applications.
 


**Insights and Recommendations** 


   - Algorithm Selection: 


      - For small datasets, prioritize genetic algorithms for their ability to explore the solution space effectively.


      - For large datasets, adopt greedy algorithms for their scalability and computational efficiency. 


      - Local search algorithms may be considered for mid-sized datasets or as a supplementary method in combination with other algorithms. 


    

  - Scalability Considerations: 


     - As dataset size increases, computational efficiency and execution time become critical factors in algorithm selection. 


     - Understanding dataset characteristics is essential to leverage the strengths of each algorithm effectively.