which should be distinguished in order to preserve a structure of the problem space that helps reaching the goal states. Here, one approach from EAs is to consider not a single objective, but to aim initially at what has been called “stepping stones” [@lehman_abandoning_2011]. This approach is based on a different representation that combines reduction of information from the high-dimensional state space with keeping an intermediate structure that is more effective in directing the system towards an optimal solution than the direct use of the given objective. In the example of finding a path through a maze, stepping stones would introduce an intermediate representation of positional space that conflates places close to each other. . Learning in such an approach is not solely aiming at optimization. Instead, such approaches try to construct an intermediate representation and aim for covering this (therefore not too high dimensional) intermediate representation with stepping stones. This is realized using forms of novelty-based search. In the example of finding a path through a maze, stepping stones would introduce an intermediate representation of positional space that conflates places close to each other. These together would be related towards the objective and when searching for a path towards 