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This project aims to use Genetic Algorithms for optimizing the Travelling Salesperson Problem.

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TSP-Genetic-Algorithm

This project aims to use Genetic Algorithms for optimizing the Travelling Salesperson Problem.

The datasets come from TSPLIB, a collection of traveling salseperson problem datasets maintained by Gerhard Reinelt at Click Here We have chosen a subset of EUC_2D examples as the dataset.

Genetic Operators Used

Selection: Process of selecting two or more parent chromosomes from a given generation of population for mating. Here we have used tournament selection.
Crossover: Operation performed on the parents to produce offsprings. Here we have used order crossover with probability pc.
Mutation: Permanent change in the sequence of DNA. Here, Swap mutation is performed with probability pm

Running Instructions

  1. Run the server using the server README.md
  2. Run the client using the client README.md

Screenshots

TSP-1

TSP-2

TSP-3

TSP-4

Current known bugs (Might be resolved in future updates)

  1. There are file parsing issues for some datasets in the getCitiesFromFile() function in TSP.py

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This project aims to use Genetic Algorithms for optimizing the Travelling Salesperson Problem.

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