Traveling Salesman Problem
A salesperson has to visit multiple cities on their trip. We use a genetic algorithm to find the shortest route.
The traveling salesman problem (TSP) asks the following question: "Given a list of cities and the distances between each pair of cities, what is the shortest possible route that visits each city and returns to the origin city?". There are many ways to solve this, but we used a genetic algorithm, which at first randomly trying different paths and then evolves to focus on the most promsing ones and discard the bad ones. Other popular solutions include simulated annealing, pairwise exchange, the Lin–Kernighan heuristic, Christofides algorithm, brute-force search, the Held–Karp algorithm, nearest neighbour search, tabu search, branch-and-bound, ant colony optimization, and swarm intelligence. The traveling salesman problem has many real-life applications including planning, logistics, and manufacturing. It can also be whimsically applied to helping Santa Clause find the best route to deliver presents to the approximately 400,000 cities, towns, and villages in the world, as finding the shorted route is critical when it all has to be done in one night.

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