# Enhanced Optimization with Composite Objectives and Novelty Pulsation

arXiv: https://arxiv.org/abs/1906.04050 · HTML: https://mlsanity.com/paper/1906.04050/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2019.

## Built on by (most cited first, 2 of 2)

- [EVOTER: Evolution of Transparent Explainable Rule-sets](https://mlsanity.com/paper/2204.10438/) (2022)
- [Asynchronous Evolution of Deep Neural Network Architectures](https://mlsanity.com/paper/2308.04102/) (2023)

## Abstract (arXiv)

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.