The video claims that, in this playground, the genetic algorithm can efficiently solve the Traveling Salesman Problem using the default parameters. The demo shows the route improving and the path length decreasing over iterations.
Conditions: The genetic-algorithm implementation is run with the displayed default parameters.; The problem instance is the one shown in the visualization.; The word "efficiently" is qualitative here; no formal efficiency metric or runtime bound is provided.
The video claims that, in this playground, the genetic algorithm can efficiently solve the Traveling Salesman Problem using the default parameters. The demo shows the route improving and the path length decreasing over iterations.
Conditions: The genetic-algorithm implementation is run with the displayed default parameters.; The problem instance is the one shown in the visualization.; The word "efficiently" is qualitative here; no formal efficiency metric or runtime bound is provided.
For a Traveling Salesman Problem instance with 304 cities, there are approximately 2.3x10624 possible unique routes. This number is derived from the factorial growth of permutations, specifically (304-1)! / 2, accounting for the fact that the starting city and direction do not matter for undirected cycles.
Conditions: The problem has 304 distinct cities.; Routes are considered as unique Hamiltonian cycles (direction and starting point do not matter).
For a Traveling Salesman Problem instance with 304 cities, there are approximately 2.3x10624 possible unique routes. This number is derived from the factorial growth of permutations, specifically (304-1)! / 2, accounting for the fact that the starting city and direction do not matter for undirected cycles.
Conditions: The problem has 304 distinct cities.; Routes are considered as unique Hamiltonian cycles (direction and starting point do not matter).