Review lab1

[maxfra01]

General comments

The code is clear and well-explained, making it easy to follow and understand. You applied two different approach: one based on simulated annealing and the other on RMHC. The simulated annealing approach is effective because it allows the algorithm to escape local optima, and provides a better exploration of the search space. I appreciate your use of a multiple tweak function based on a mask.

In the second approach i find your weighted_mutation function particularly interesting because it allows the algorithm to tweak sets based on their costs. Additionally, your algorithm has a self-adaptive strength parameter which enables a dynamic mutations.

These are good ideas that facilitate the algorithm's ability to self-learn and better handle the problem.

One possible improvement could be mixing the two strategies you employed by including your self-adaptive strength into simulated annealing; this could enhance exploration. I also noticed that you started with an all True solution, which is valid, but starting from a random solution could significantly reduce computational waste and improve efficiency.

Overall, great job!

[Andrea-1704]

Thanks for the review. It was incredibly helpfull and the suggestion to merge the 2 strategies was very effective!