deceptive problem can be nicely illustrated using the problem of solving a maze. Optimization approaches in general try to string together a sequence of movements (the lower-order building blocks). For reaching a goal location, spatial distance between current and target location has often been used as a simple and effective objective. But in the case of a maze, the single objective of distance towards goal appears too narrow to distinguish between different actions. With a single distance-based objective all locations on a circle around the goal are grouped together and evaluated as having the same value. But this conflation of states and location is a poor aid when trying to find a path through such a maze. The problem is deceptive as moving towards points closer to the goal might actually lead the learning system into a dead-end (for details see [@lehman_abandoning_2011]).

Lehman and Stanley [@lehman_abandoning_2011] pointed out that in such an approach stepping stones towards a goal are not rewarded, instead the focus is solely on a single objective which leads the search into local minima. The main problem in deceptive problems is that the chosen objective lumps together quite different states, in the maze example quite different locations, 