Self Replication to Preserve Innate Learned Structures in Artificial Neural Networks

It’s interesting to investigate combination of deep neuro-evolution and self-replication1 to evolve Artificial Neural Networks (ANNs) able to keep and complexify innate learned structures aimed to fulfill auxiliary tasks (orthogonal to the self-replication). In such a way, it may became possible to build evolutionary lineage tree of ANNs specialized to complete specific tasks under different environmental settings through subsequent training sessions. And the knowledge acquired during all these training sessions will accumulate not only in the form of learned connections’ weights, but in a neural network’s topology as well.

Go NEAT Novelty Search

This project provides GOLang implementation of Neuro-Evolution of Augmented Topologies (NEAT) with Novelty Search optimization aimed to solve deceptive tasks with strong local optima