Machine Learned Learning Machines
  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. Though these are methods that typically
operate separately, we combine evolutionary adaptation and machine learning
into one approach. Our focus is on machines that can learn during their
lifetime, but instead of equipping them with a machine learning algorithm we
aim to let them evolve their ability to learn by themselves. We use evolvable
networks of probabilistic and deterministic logic gates, known as Markov
Brains, as our computational model organism. The ability of Markov Brains to
learn is augmented by a novel adaptive component that can change its
computational behavior based on feedback. We show that Markov Brains can indeed
evolve to incorporate these feedback gates to improve their adaptability to
variable environments. By combining these two methods, we now also implemented
a computational model that can be used to study the evolution of learning.
