The pole balancing problem from the viewpoint of system flexibility

Whereas evolutionary computation usually solves problems from scratch, organisms evolve under changing environments and possess flexibility, adapting from being good at one task to being good at a related task. There is abundant evidence that there are general properties that promote flexibility in nature, such as hierarchy, modularity, exploratory behavior, and degeneracys or neutrality.
Our interest is to understand if such properties can also be identified for non-biological systems. We thus study if a controller evolved by a genetic algorithm for one pole balancing task can be adapted to a different pole balancing task, and if this saves training time compared to evolving a new controller from scratch. Moreover, we investigate how diversity and degeneracy in the controllers population affect adaption efficiency by promoting high quality solutions that are both structurally and behaviorally diverse, concluding that it can potentially decrease the adaption cost.