Solving TSP using Genetic Algorithm - Sabry's Dialy Case Study

Introduction

This project aims to solve the Traveling Salesman Problem (TSP) using a Genetic Algorithm. The TSP involves finding the shortest possible route that visits a set of locations (in this case, Egyptian governorates) and returns to the starting location. We'll apply this algorithm to help Sabry's Dialy, an Egyptian YouTuber, find the closest road between Egyptian governorates for his journey.

Case Study Background

Sabry's Dialy embarked on a journey to visit all the governorates of Egypt. However, due to a business trip abroad after reaching governorate 19, he needed to find the quickest route to complete his journey efficiently. He contacted us via LinkedIn, and we took on the challenge to optimize his route.

Algorithm Overview

We employ a Genetic Algorithm to tackle the TSP in this project. Genetic Algorithms are a popular choice for solving combinatorial optimization problems like the TSP. They use concepts inspired by the process of natural selection to evolve a population of potential solutions over generations until an optimal or near-optimal solution is found.

Getting Started

To use this project, follow these steps:

Clone the Repository:

git clone https://github.com/AhemdMahmoud/Solving_TSP_using_GA.git

Install Dependencies: Make sure you have the necessary dependencies installed. You might need Python and relevant libraries for this project.

Run the Code: Execute the main script to solve the TSP for Sabry's Diary's case study.

Usage

Describe how to use your code to solve the TSP. Provide clear instructions and possibly some code snippets.

Results

Discuss the results of applying the Genetic Algorithm to Sabry's Diary's case. Include statistics such as the optimized route, distance covered, and any other relevant metrics.

Contributing

Explain how others can contribute to this project, whether through code contributions, bug reporting, or feature suggestions.