https://github.com/mvazramos/ga_tsp_simple

Simple Genetic Algorithm to solve the Travelling Salesman problem. This mini-project was done for my Combinatorial Optimization course.
https://github.com/mvazramos/ga_tsp_simple

genetic-algorithm python

Last synced: 8 months ago
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Simple Genetic Algorithm to solve the Travelling Salesman problem. This mini-project was done for my Combinatorial Optimization course.

• Host: GitHub
• URL: https://github.com/mvazramos/ga_tsp_simple
• Owner: mvazramos
• License: mit
• Created: 2021-06-10T10:07:22.000Z (over 4 years ago)
• Default Branch: main
• Last Pushed: 2021-06-10T10:27:23.000Z (over 4 years ago)
• Last Synced: 2025-01-02T15:32:26.659Z (10 months ago)
• Topics: genetic-algorithm, python
• Language: Jupyter Notebook
• Homepage:
• Size: 410 KB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Simple Genetic Algorithm Implementation to solve the **Travelling Salesman Probelm**

This was a project for my *Combinatorial Optimization* course.

I did a very simple implementation. The time I had to perform the project was not enough to play a bit with different operators and different strategies.

The implementation is mostly procedural, just implemented a Weighted Graph data structure that would be useful.

( Multiple runs should be executed in order to obtain the best solution. There is no guarantee that the algorithm will return the global optimum, and may fall into a local optimum. My stopping criterion was the number of iterations. If considered differences between consecutive solutions it might converge too early and fall more easy into a local solution)

Besides the code, I also provide the project report in Portuguese, a notebook with the implementation and an R script using the `library(TSP)` which I used to check how bad/good my implementation was.