2 research outputs found
Enhanced Optimization with Composite Objectives and Novelty Pulsation
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.Comment: arXiv admin note: substantial text overlap with arXiv:1803.0374
Creative AI Through Evolutionary Computation: Principles and Examples
The main power of artificial intelligence is not in modeling what we already
know, but in creating solutions that are new. Such solutions exist in extremely
large, high-dimensional, and complex search spaces. Population-based search
techniques, i.e. variants of evolutionary computation, are well suited to
finding them. These techniques make it possible to find creative solutions to
practical problems in the real world, making creative AI through evolutionary
computation the likely "next deep learning."Comment: This is an extended version of arXiv:1901.0377