Thank you for your suggestion! However, I couldn't access it easily while reviewing the code. As a primary recommendation, I suggest including such suggestions directly in the README instead of opening an issue for yourself.
Minor comment
I appreciate your effort to combine many of the concepts covered in class, and overall, I like the approach you've taken. That said, the solution is not "plug-and-play," which makes it more challenging for me to evaluate your code. Improving usability would be beneficial.
Major comment
Algorithm Design: Your choice to keep the probabilities for mutation, crossover, and other transformations fixed is reasonable. However, I would like to see a comparison with adaptive probabilities, which might improve the algorithm’s convergence.
Result Presentation: While I appreciate the scatter plot provided at the end, the results are not easy to interpret. To improve clarity, consider creating a table that compares your implementation with the professor's version or different variations of your implementation. This would make it easier to assess the performance of your algorithm.
Thanks for the review! i'll try to implement those suggestions
What do you mean by the solution is not "plug-and-play"? how can i improve usability?
if it's not a problem we can discuss it at wednesday lab
General comment
Thank you for your suggestion! However, I couldn't access it easily while reviewing the code. As a primary recommendation, I suggest including such suggestions directly in the README instead of opening an issue for yourself.
Minor comment
I appreciate your effort to combine many of the concepts covered in class, and overall, I like the approach you've taken. That said, the solution is not "plug-and-play," which makes it more challenging for me to evaluate your code. Improving usability would be beneficial.
Major comment
Algorithm Design: Your choice to keep the probabilities for mutation, crossover, and other transformations fixed is reasonable. However, I would like to see a comparison with adaptive probabilities, which might improve the algorithm’s convergence.
Result Presentation: While I appreciate the scatter plot provided at the end, the results are not easy to interpret. To improve clarity, consider creating a table that compares your implementation with the professor's version or different variations of your implementation. This would make it easier to assess the performance of your algorithm.