GeneticAlgorithm_TSP_Demo

Python implementation of a genetic algorithm for Traveling Salesman Problem optimization.

Why I Built This

I built this to move beyond textbook descriptions of genetic algorithms and see how convergence actually behaves in code. TSP offered a clean way to test selection, crossover, and mutation choices under clear optimization pressure. It became a practical sandbox for understanding how small hyperparameter changes affect solution quality over time.

Problem Setup

Solve TSP on a large fully connected graph, typically around 50 nodes.

Implementation Highlights

  • Euclidean coordinate generation to satisfy triangle-inequality constraints.
  • Route-based population initialization and fitness as inverse path distance.
  • Roulette-style parent selection with ordered crossover.
  • Configurable mutation and generation controls for experimentation.

Outcome

A practical notebook demo that shows how simple genetic operators can steadily improve route quality over generations.