I wonder, if there are other than NEAT approaches to evolving architectures and weights of artificial neural networks?

To be more specific: I am looking for projects/frameworks/libraries that use evolutionary/genetic algorithms to simultanousely evolve both topology and train weights of ANNs other than NEAT approach. By 'other' I mean similar to NEAT but not based entirely on NEAT. I hope to find different approaches to the same problem.


The Wikipedia article on neuroevolution contains a list of neuroevolution techniques (e.g. NEAT). I will list below the examples that evolve both the parameters and the topology of the neural network.

  • $\begingroup$ I would like to note that I am not familiar with any of these methods. This list is based on the info in the mentioned Wikipedia article. $\endgroup$ – nbro Jan 25 '20 at 15:53

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