feature selection using genetic algorithm in Python? I have a dataset of 4712 records and 60+ features working on a binary classification problem. I already tried out all the feature selection approaches like filter, embedded and wrapper but am just curious to learn and try genetic algorithm for feature selection.

The reason for choosing genetic algorithm is because I guess it will just provide me the best model fit based on best features.

1) I understand it might take time but would you people help me know how can I do this in Python?

2) In addition, is genetic algorithm any different or better than all other feature selection approaches discussed above? What are its disadvantages?

Is there any python packages and tutorial available on how to use this?

I see tutorials but they all about the theory of genetic algorithm

Can you help me by sharing any tutorial or package for genetic algorithm?

post update
Feature selection is a combinatorial optimization problem. And genetic algorithms is an optimization technique.

So there really isn't anything special, you just need to formulate your problem as an optimization one, and understand how do genetic algorithms optimize. There are enough tutorials on this.

Whether it's better or worse you already know the answer. It depends. On the dataset, constraints etc. What I can tell you from experience is that


You can not expect it to blow your mind but they do work pretty well
They are a great ensembler, meaning results are pretty different (yet accurate) from tree-based methods, NN etc...


Finally regarding implementation, here is completely (maybe too much) automated library based on genetic programming. (notice the word programming here referring to optimization not writing code) Also, it covers feature selection.