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Genetic algorithms for training deep neural networks (2017)
21–23 of 23 posts
Re: Genetic algorithms for training deep neural networks (2017)
#22The article is from last year but it's still extremely valuable and interesting. Exploring this topic is currently my primary hobby. Specifically, I've been using OpenAI's retro (Sonic, Contra, Mario, Donkey Kong and, more recently FZero) and comparing the ancient NEAT with more fashionable stuff like DQN, PPO, A3C and DDPG. With my extremely limited experience, NEAT seems to outperform all of these other algorithms.…
You are evolving the topology, but using regular gradient descent/backprop for any given network, correct?
Yeah, topology and weights. It's highly subject to initial conditions. You almost need another NEAT network to evolve the initial conditions. I believe it's turtles all the way down.
Re: Genetic algorithms for training deep neural networks (2017)
#23I tried training small RNN models using GA around 1990. I had lunch with John Koza (the genetic programming pioneer) and he suggested that it was an interesting idea but would not scale. The Uber team, my controlling mutation, got it to scale - good for them. I did a few years later use this as an example in my book "C++ Power Paradigms", McGraw-Hill 1994 (Genetic Algorithms, Neural Networks, and Constraint Programming).