  In Evolutionary Robotics a population of solutions is evolved to optimize
robots that solve a given task. However, in traditional Evolutionary
Algorithms, the population of solutions tends to converge to local optima when
the problem is complex or the search space is large, a problem known as
premature convergence. Quality Diversity algorithms try to overcome premature
convergence by introducing additional measures that reward solutions for being
different while not necessarily performing better. In this paper we compare a
single objective Evolutionary Algorithm with two diversity promoting search
algorithms; a Multi-Objective Evolutionary Algorithm and MAP-Elites a Quality
Diversity algorithm, for the difficult problem of evolving control and
morphology in modular robotics. We compare their ability to produce high
performing solutions, in addition to analyze the evolved morphological
diversity. The results show that all three search algorithms are capable of
evolving high performing individuals. However, the Quality Diversity algorithm
is better adept at filling all niches with high-performing solutions. This
confirms that Quality Diversity algorithms are well suited for evolving modular
robots and can be an important means of generating repertoires of high
performing solutions that can be exploited both at design- and runtime.
