  Our society is increasingly fond of computational tools. This phenomenon has
greatly increased over the past decade following, among other factors, the
emergence of a new Artificial Intelligence paradigm. Specifically, the coupling
of two algorithmic techniques, Deep Neural Networks and Stochastic Gradient
Descent, thrusted by an exponentially increasing computing capacity, has and is
continuing to become a major asset in many modern technologies. However, as
progress takes its course, some still wonder whether other methods could
similarly or even more greatly benefit from these various hardware advances. In
order to further this study, we delve in this thesis into Evolutionary
Algorithms and their application to Dynamic Neural Networks, two techniques
which despite enjoying many advantageous properties have yet to find their
niche in contemporary Artificial Intelligence. We find that by elaborating new
methods while exploiting strong computational resources, it becomes possible to
develop strongly performing agents on a variety of benchmarks but also some
other agents behaving very similarly to human subjects on the video game
Shinobi III : Return of The Ninja Master, typical complex tasks previously out
of reach for non-gradient-based optimization.
