TSP Simulator

This TSP Simulator implements a Genetic Algorithm to solve the Traveling Salesman Problem. It allows users to adjust key settings, including mutation rate, mutation rate decay, population size, and tournament size. The mutation rate controls how often genetic changes occur in each generation, while mutation rate decay gradually decreases this rate over time for better convergence. Population size determines the number of potential solutions in each generation, and tournament size defines how many nodes are compared to find the best match. This flexibility enables fine-tuning of the algorithm for faster or more optimal solutions. The simulator provides a visual representation of the evolving paths and allows users to observe the performance of the algorithm with different parameters. It’s an excellent tool for experimenting with genetic algorithms in solving real-world optimization problems.

#TSP #TravelingSalesman #GeneticAlgorithm #Optimization #MachineLearning #Simulation #MutationRate #PopulationSize #AlgorithmVisualization #ComputationalGeometry