Neuroevolution is a method for optimizing neural network weights and topologies using evolutionary computation. It is particularly useful in sequential decision tasks that are partially observable (i.e. POMDP), and where the state and action spaces are large (or continuous). Our work focuses on theory, algorithms, and applications of neuroevolution as described in numerous papers and demos in this site.