This Unity project can be used to visualize solving the Travelling Salesman Problem (TSP) using an implementation of the Genetic Algorithm.
Clone or download the project and open it in Unity (File → Open Project).
The following variables can be adjusted in the Unity inspector by clicking on the World object in the hierarchy.
cityCount- The number of cities in a route (default: 20)populationSize- The number of individuals in the population (default: 18)mutationRate- The probability of each city in the DNA of a chromosome to be mutated (default: 0.013)elitism- The number of best chromosomes which are transferred to the next generation (default: 6)mutateElites- When true, chromosomes chosen as elites are mutated (default: false)eliteMutationRate- The probability of each city in the DNA of an elite chromosome to be mutated (default: 0.01)itersToFindNewCity- The maximum number of iterations to find a new random city during mutation in case two identical cities are chosen (default: 5)crossoverType- The selected method of crossing over two chromosomes (default: ERX), available options: Edge Recombination Crossover (ERX) and OX (Ordered Crossover)
Note: When mutateElites is set to false, the best route in the current generation and the all-time best route will look the same. This is because the amount of randomness in the population introduced by mutation is low.