Dim Red Glow

A blog about data mining, games, stocks and adventures.

The genetic algorithm delights don’t stop

And I’m back, and so quickly! So the improvements have started....

I tried the least squares weighting of answers ... this works but I think isn’t worth doing while training. I’ll do it on the final submission but train without it.

i've just gotten the layers implemented and that seems to work but it’s too early to know if gains are really there. I think maybe if anything this will allow the code to improve to much greater extents faster instead getting bogged down at a lower score. (Though code without layers may eventually get there too)

The final improvement is essentially heuristic modeling to be applied to the odds of any given thing happening. I did this a little a while back and have rethought what I said since last post. I think the big thing is to just balance the odds of feature/channel selection and function/method/mechanism selection. This should increase speed and accuracy.

I'm still amused by the idea of giving a result to a Kaggle contest without giving the technology. I mean I give the the algorithm it generated but not how you get there. It would be delightful to win a few contests in a row without actually giving the technology away. It would turn Kaggle on its head (especially since its not the sort of thing that translates in to  kernel)

once everything is working the last step is to migrate it to a video card runnable code so I can scale it massively.