Abstract
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 tradeoffs 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-line 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.
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Appendix
Appendix
The graph of the new world record for 20-line sorting network, which moved the previous record of 92 comparators also discovered by evolution [40] down to 91.
One of the nice properties of Novelty Pulsation Method is the ability to converge with a very small pool size (like only 30 individuals in case of sorting networks). However, it still took almost 2 months to break the world record on the 20-line network running on a single machine (Fig. 14.11). Interestingly, even if it takes the same number of generations for the other methods to get there with a normal pool size of a thousand, those runs will take almost 5 years to converge!
The new 20-line sorting network with 91 comparators, discovered by novelty pulsation
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Shahrzad, H. et al. (2020). Enhanced Optimization with Composite Objectives and Novelty Pulsation. In: Banzhaf, W., Goodman, E., Sheneman, L., Trujillo, L., Worzel, B. (eds) Genetic Programming Theory and Practice XVII. Genetic and Evolutionary Computation. Springer, Cham. https://doi.org/10.1007/978-3-030-39958-0_14
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