Artificial cognitive systems (e.g., artificial neural networks) have taken an ever more present role in the modern world, providing enhancements to everyday life in our cars, in our phones, and on the internet. In order to produce systems more capable of achieving their designated tasks, previous work has sought to direct the evolution of networks using a process referred to as R-augmentation. This process selects for the maximisation of an information-theoretic measure of the agent's stored understanding of the environment, or its representation (R) in addition to selecting for task performance. This method was shown to induce increased task performance in a shorter amount of evolutionary time compared to a standard genetic algorithm. Extensions of this work have looked at how R-augmentation affects the distribution of representations across the neurons of the brain ”tissue” or nodes of the network, referred to as smearedness (S). Here we seek to improve upon the prior methods by moving beyond the simple maximization used in the original augmentation formula by using the MAP-Elites algorithm to identify intermediate target values to optimize towards. We also examine the feasibility of using MAP-Elites itself as an optimization method as opposed to the traditional selection methods used with R-augmentation, to mixed success. These methods will allow us to shape how the network evolves, and produce better-performing artificial cognitive systems.