  An important benefit of multi-objective search is that it maintains a diverse
population of candidates, which helps in deceptive problems in particular. Not
all diversity is useful, however: candidates that optimize only one objective
while ignoring others are rarely helpful. A recent solution is to replace the
original objectives by their linear combinations, thus focusing the search on
the most useful trade-offs between objectives. To compensate for the loss of
diversity, this transformation is accompanied by a selection mechanism that
favors novelty. This paper improves this approach further by introducing
novelty pulsation, i.e. a systematic method to alternate between novelty
selection and local optimization. In the highly deceptive problem of
discovering minimal sorting networks, it finds state-of-the-art solutions
significantly faster than before. In fact, our method so far has established a
new world record for the 20-lines sorting network with 91 comparators. In the
real-world problem of stock trading, it discovers solutions that generalize
significantly better on unseen data. Composite Novelty Pulsation is therefore a
promising approach to solving deceptive real-world problems through
multi-objective optimization.

Neural and Evolutionary Computing