Code developed for the Computational Intelligence course held by Professor Giovanni Squillero at Politecnico di Torino.
I implemented an A* search to find the solution with the least amount of sets possible. The heuristic function is the number of uncovered elements. The cost function is the number of sets used.
A challenge on single state methods proposed by Squillero for the Halloween weekend. The summary contains a few of the information that are found in the more complete notebook.
Not completed before the deadline
I completed the lab after the deadline, in preparation for the exam. The code is in the post-deadline branch of this repository.
Done in collaboration with @giovanni-violo
Wrote a local-search algorithm using Evolutionary Programming able to solve the Problem instances 1, 2, 5, and 10 on a 1000-loci genomes, using a minimum number of fitness calls.
Not completed before the deadline
The exam final project. I developed a Minmax solution with alpha beta pruning for the game Quixo. The project contains a report with a description of the algorithm, the evaluation function, the pruning and the results of the tests. The game can be played in the terminal. The project was developed in Python 3.11.5 and tested on Windows 11 using the Poetry package manager. The project should work on any OS and Python 3.8+ but this has not been tested.