Evolution of evolvability for neuroevolution

Scientists have been in awe of the powers of evolution since comprehending the fundamental
principles of natural selection. As Darwin eloquently put it, nature has given rise to
“endless forms most beautiful”. However, beyond the mesmerizing beauty of nature, lies an
even more fascinating aspect - the creation of human intelligence. Although AI research has
produced impressive outcomes in the past decade, the techniques employed to accomplish
these outcomes diverge considerably from the natural process of brain evolution. The
primary dissimilarity stems from the fact that nature employs the evolution of DNA code,
which encodes the instructions for brain development, whereas mainstream AI represents
all brain parameters directly. This disparity is noteworthy as the indirect representation
utilized by nature confers upon it a potent ability to enhance evolvability over time, a
feature that is absent in direct representation. This potential stems from the ability to
choose instructions in a manner that renders the brain resistant to change in certain
directions while facilitating easy modification in other directions.
The present thesis delves into the potential of leveraging indirect encoding and evolvability
during the neural network evolution process. To this end, we propose two algorithms,
namely Quality Evolvability ES and Evolvability Map Elites, which enable direct selection
for evolvability. We conduct an evaluation of the efficacy of these algorithms in the context
of robotics locomotion tasks. Additionally, we investigate the necessary conditions for
indirect encoding to be useful for learning and conduct experiments to verify our hypothesis
in the domain of image recognition tasks