  Neuroevolution is a process of training neural networks (NN) through an
evolutionary algorithm, usually to serve as a state-to-action mapping model in
control or reinforcement learning-type problems. This paper builds on the Neuro
Evolution of Augmented Topologies (NEAT) formalism that allows designing
topology and weight evolving NNs. Fundamental advancements are made to the
neuroevolution process to address premature stagnation and convergence issues,
central among which is the incorporation of automated mechanisms to control the
population diversity and average fitness improvement within the neuroevolution
process. Insights into the performance and efficiency of the new algorithm is
obtained by evaluating it on three benchmark problems from the Open AI platform
and an Unmanned Aerial Vehicle (UAV) collision avoidance problem.
