how to build a good test bed?

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From memory, population size was about 20. It was something between a gradient descent and a genetic algorithm, by moving from the stronger members away from the weaker members, plus some random component. Remember, I had already extracted all of the features etc, and saved them just before inserting into the Kd-Tree, so the only thing I needed at evaluation time was:

  1. read data from file
  2. add points to the tree
  3. KNN/KDE
  4. count inliers vs outliers -> give a score

Then at the end multiply the evolved weights with the code weights, recompile, and collect a new set of data; repeat until happy.

Skilgannon (talk)22:56, 3 October 2017

Just to clarify, maybe not in the classic way, but your algo is more a elitist than a mutator, is that it?

Rsalesc (talk)23:07, 3 October 2017
 

I'm doing exactly that, just ran a generation of pop. size 30 on top of 155 battles from top bots of the Rumble and it took me about 15 minutes. I'll debug what is taking this much time later. Thanks for your help :)

Rsalesc (talk)23:29, 3 October 2017

Make sure you are only simulating aiming on waves that you actually fired.

Skilgannon (talk)11:10, 12 October 2017
 

So are you using something similar to PBIL instead of traditional GAs?

Xor (talk)03:33, 4 October 2017

Cool, I have to put my hands on some ML texts :)

Rsalesc (talk)12:34, 4 October 2017
 

I think the closest would be something between gradient descent and stochastic learning.

Skilgannon (talk)11:09, 12 October 2017

So stochastic gradient descent?

Xor (talk)16:05, 12 October 2017

Still not quite, because it uses a population like GA does, and used linear combinations between the population to estimate gradient similarly to how gradient descent would. Honestly, there were probably better/faster algorithms that would have worked better out-the-box, but this worked fine, it just took a bit longer.

Skilgannon (talk)17:32, 12 October 2017

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