How does novelty search work? In this article, the author claims that guiding evolution by novelty alone (without explicit goals) can solve problems even better than using explicit goals. In other words, using a novelty measure as a fitness function for a genetic algorithm works better than a goal-directed fitness function. How is that possible?
As explained in an answer to this AI SE question, GAs are 'satisficers' rather than 'optimizers' and tend not to explore 'outlying' regions of the search space. Rather, the population tends to cluster in regions that are 'fairly good' according to the fitness function.

In contrast, I believe the thinking is that novelty affords a kind of dynamic fitness, tending to push the population away from previously discovered areas.