Travelling Salesman

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About

What is the Travelling Salesman Problem?

Imagine a salesman who needs to visit multiple cities. The goal is to find the shortest possible route that visits each city exactly once and returns to the starting city. This is known as the Travelling Salesman Problem (TSP).

How Does This App Work?

This app uses a genetic algorithm to approximate a solution to the TSP. Genetic algorithms are inspired by natural selection. They work by creating a population of random solutions, evaluating their fitness (in this case, the total distance of the route), and then breeding new solutions based on the best-performing ones.

Control Your Simulation

You can customize the simulation using the following parameters:

• Location Count
  Determines the number of cities the salesman must visit.
• Layout
  Choose between 'Random' to position cities randomly or 'Circle' to arrange them in a circle.
• Population
  Sets the number of potential solutions the algorithm will consider in each generation.
• Crossover Rate
  Controls how often genetic information is exchanged between solutions during breeding.
• Mutation Rate
  Determines the likelihood of random changes in a solution, introducing diversity.
• Elitism Rate
  Determines what percentage of the top solutions to copy into the next generation unmodified.

By adjusting these parameters, you can experiment with different conditions and observe how the genetic algorithm finds solutions.

Want to learn more about genetic algorithms?

This video is a good introduction: Genetic Algorithms Explained By Example - Kie Codes