This repository contains all the materials and documentation related to my experiences and projects in the Computational Intelligence course. As a student, I was deeply engaged with the course material, which explored various techniques and approaches for creating intelligent systems.
Hi Antonio! I chose your code for my review because I was impressed with your solution, so I wanted to know all the details of how you achieved it.
First of all, good job on the readme, which explains everything you used very clearly, and also gives me an idea of what to look for in the code.
I found it interesting how you select your initial population by having a genome with all 0’s, therefore relying mostly on mutation to develop the first few generations. With this, I think you might be able to achieve even better solutions (they’re still very good as is!) by having a higher mutation chance in the first few generations, maybe by having it as a function that decreases exponentially so it settles rather quickly. I am also curious as to how you chose this particular mutation chance of 0.3.
Did you experiment with more crossover options? I found with my code that one-point crossover wasn’t the best function for my purposes, so maybe if you have the time it could be worth trying different approaches to the function.
Your code is clear and concise, very well written, and I didn’t find any issues with it. It seems that your choices of parameters, fitness and crossover led you to a great solution, so I want to congratulate you on that! I am sure it took a lot of work and experimenting, so it’s always nice to see when it pays off.
Hi Antonio! I chose your code for my review because I was impressed with your solution, so I wanted to know all the details of how you achieved it.
First of all, good job on the readme, which explains everything you used very clearly, and also gives me an idea of what to look for in the code.
I found it interesting how you select your initial population by having a genome with all 0’s, therefore relying mostly on mutation to develop the first few generations. With this, I think you might be able to achieve even better solutions (they’re still very good as is!) by having a higher mutation chance in the first few generations, maybe by having it as a function that decreases exponentially so it settles rather quickly. I am also curious as to how you chose this particular mutation chance of 0.3.
Did you experiment with more crossover options? I found with my code that one-point crossover wasn’t the best function for my purposes, so maybe if you have the time it could be worth trying different approaches to the function.
Your code is clear and concise, very well written, and I didn’t find any issues with it. It seems that your choices of parameters, fitness and crossover led you to a great solution, so I want to congratulate you on that! I am sure it took a lot of work and experimenting, so it’s always nice to see when it pays off.
Have a nice day and keep up the good work ;)