In a variety of problem domains, such as math and motion planning, humans use a dual strategy of generation and recall to find solutions. 'Generation' uses production rules and models to search for novel solutions to novel problems, whereas 'recall' reuses previously found solutions for similar previously encountered problems. As we expect the advantages of this dual strategy to carry over to the robotics domain, we compare and evaluate generation and recall strategies for motion planning on a set of reaching tasks. The specific implementations we use are the lazy variant of the Rapidly-exploring Random Trees and Dynamic Movement Primitives, and we compare these two methods on the commercially available REEM robot. Quantifying the differences and advantages of these methods constitutes is required to make informed decisions about which approach is most suitable for which application domain and task contexts.