Python Evolutionary Algorithm for TSP Solution (No AI, Commented Code, Simple Style)
Budget: €15 – €20 EUR
Project Overview
I'm looking for a Python developer to implement an evolutionary algorithm to solve a Traveling Salesman Problem (TSP) based on a given distance matrix. The final code should use basic Python only, be cleanly structured, and clearly commented in simple English.
This is not a production-grade system – the goal is a clear and understandable solution, written in a student-level coding style.
The algorithm should operate with a (100, 100)-strategy (100 parents → 100 children per generation), based on a fixed distance matrix loaded from a .pkl file.
The main components include mutation, selection, crossover and a stopping condition based on stagnation.
Requirements
- Implement an evolutionary algorithm (e.g. mutation, crossover, selection logic)
- Use only the provided libraries in the template
The solution must be:
- Manually written – strictly no AI tools (ChatGPT, Copilot etc.)
- Well-commented and logically organized
- Structured in a way that looks like it was written by a beginner-to-intermediate Python user
- Output: a single .ipynb file that runs without setup
Deadline
- 36 hours max from project start
- Earlier delivery highly appreciated
What Not to Do
- No use of AI-generated code or assistance
- No advanced libraries or frameworks
When Applying Please confirm:
- You’ll write the code manually without AI.
- You can deliver within 48 hours.
- You’ll provide clear, commented code.
I'm looking for a Python developer to implement an evolutionary algorithm to solve a Traveling Salesman Problem (TSP) based on a given distance matrix. The final code should use basic Python only, be cleanly structured, and clearly commented in simple English.
This is not a production-grade system – the goal is a clear and understandable solution, written in a student-level coding style.
The algorithm should operate with a (100, 100)-strategy (100 parents → 100 children per generation), based on a fixed distance matrix loaded from a .pkl file.
The main components include mutation, selection, crossover and a stopping condition based on stagnation.
Requirements
- Implement an evolutionary algorithm (e.g. mutation, crossover, selection logic)
- Use only the provided libraries in the template
The solution must be:
- Manually written – strictly no AI tools (ChatGPT, Copilot etc.)
- Well-commented and logically organized
- Structured in a way that looks like it was written by a beginner-to-intermediate Python user
- Output: a single .ipynb file that runs without setup
Deadline
- 36 hours max from project start
- Earlier delivery highly appreciated
What Not to Do
- No use of AI-generated code or assistance
- No advanced libraries or frameworks
When Applying Please confirm:
- You’ll write the code manually without AI.
- You can deliver within 48 hours.
- You’ll provide clear, commented code.