Can neuroevolution of augmenting topologies (NEAT) neural networks be built in TensorFlow?
I am making a machine learning program for time series data analysis and using NEAT could help the work. I started to learn TensorFlow not long ago but it seems that the computational graphs in TensorFlow are usually fixed. Is there tools in TensorFlow to help build a dynamically evolving neural network? Or something like Pytorch would be a better alternative? Thanks.

It can't be implemented in the static graph mode of TensorFlow without significant tradeoffs because the topology of the neural networks in the population changes. Static graphs are suited for models whose architecture doesn't change during training. However, it can be done in TensorFlow Eager or PyTorch because they support dynamic computation graphs. 

Check this implementation in TensorFlow Eager: https://github.com/crisbodnar/TensorFlow-NEAT
