Open-source tools or libraries to experiment with neural networks and evolutionary algorithms I'd like to do some experimenting with neural net evolution (NEAT). I wrote some GA and neural net code in C++ back in the 90s just to play around with, but the DIY approach proved to be labor-intensive enough that I eventually dropped it.
Things have changed a lot since then, and there are lots of very nice open-source libraries and tools around for just about any interest one might have. I've Googled different open-source libraries (e.g. DEAP), but I could use some help choosing one that would be a good fit.

I spent much of my time writing code to visualize what was going on (neural net state, population fitness) or final results (graphs, etc).
Maybe this would have to be fulfilled by a separate open-source library, but visualization support would be something that would allow me to spend more time on the problem/solution and less on implementation details.

I know C/C++, Java, C#, Python, Javascript and a few others. Something that's a nice trade-off between a higher-level language and good performance on home hardware would be a good choice.


Can someone with experience suggest a good open-source library or set of tools?
https://github.com/wagenaartje/neataptic

As this is written in Javascript and does not (yet) offer GPU support, it is quite slow. However, it is very nice to fiddle around with flexible network architectures. The only visualisation that it offers right now is a map of network architecture, but graphs could easily be implemented.