Evolving autonomous learning in cognitive networks.

ABSTRACT: There are two common approaches for optimizing the performance of a machine: genetic algorithms and machine learning. A genetic algorithm is applied over many generations whereas machine learning works by applying feedback until the system meets a performance threshold. These methods have been previously combined, particularly in artificial neural networks using an external objective feedback mechanism. We adapt this approach to Markov Brains, which are evolvable networks of probabilistic and deterministic logic gates. Prior to this work MB could only adapt from one generation to the other, so we introduce feedback gates which augment their ability to learn during their lifetime. We show that Markov Brains can incorporate these feedback gates in such a way that they do not rely on an exter

SUBMITTER: Sheneman L 

PROVIDER: S-EPMC5711912 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature