It is important on the start of the AI journey to understand basic algorithms, their functions and limitations on current data or task we're going to be working with. That's why this assignment present 9 different structures to put them to the test and comparison, and depending on results, see which one is more effricient for this problem. Have in mind that we're doing a all 9 tests for an specific problem called "Travel Salesman Problem (TSP).
Known as an optimization challenge asking the shortest possible route to visit a list of cities only once and return to the origin city.
For each algorithm, it was provide a functional and well-documented implementation.
- Random walk
- Hill climbing
- Randomized hill climbing
- Gradient ascent (steepest ascent)
- Gradient descent
- Simulated annealing
- Eight queens problem
- Map coloring
- Magic triangle
On the notebook provided with the structure and solutions, you will find little descriptions of the functions and the final comparison between the 9 algorithms tested. 😀