Serendipitous Scaffolding to improve a Genetic Algorithm’s Speed and Quality

A central challenge to evolutionary computation is enabling techniques to evolve increasingly complex target end products. Frequently direct approaches that reward only the target end product itself are not successful because the path between the starting conditions and the target end product traverses through a complex fitness landscape, where the directly accessible intermediary states may be require deleterious or even simply neutral mutations. As such, a host of techniques have sprung up to support evolutionary computation techniques taking these paths. One technique is scaffolding where intermediary targets are used to provide a path from the starting state to the end state. While scaffolding can be successful within well-understood domains it also poses the challenge of identifying useful intermediaries. Within this paper we first identify some shortcomings of scaffolding approaches — namely, that poorly selected intermediaries may in fact hurt the evolutionary computation’s chance of producing the desired target end product. We then describe a light-weight approach to selecting intermediate scaffolding states that improve the efficacy of the evolutionary computation.