تقرير
Density Descent for Diversity Optimization
| العنوان: | Density Descent for Diversity Optimization |
|---|---|
| المؤلفون: | Lee, David H., Palaparthi, Anishalakshmi V., Fontaine, Matthew C., Tjanaka, Bryon, Nikolaidis, Stefanos |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing |
| الوصف: | Diversity optimization seeks to discover a set of solutions that elicit diverse features. Prior work has proposed Novelty Search (NS), which, given a current set of solutions, seeks to expand the set by finding points in areas of low density in the feature space. However, to estimate density, NS relies on a heuristic that considers the k-nearest neighbors of the search point in the feature space, which yields a weaker stability guarantee. We propose Density Descent Search (DDS), an algorithm that explores the feature space via CMA-ES on a continuous density estimate of the feature space that also provides a stronger stability guarantee. We experiment with DDS and two density estimation methods: kernel density estimation (KDE) and continuous normalizing flow (CNF). On several standard diversity optimization benchmarks, DDS outperforms NS, the recently proposed MAP-Annealing algorithm, and other state-of-the-art baselines. Additionally, we prove that DDS with KDE provides stronger stability guarantees than NS, making it more suitable for adaptive optimizers. Furthermore, we prove that NS is a special case of DDS that descends a KDE of the feature space. Comment: 15 pages, 5 figures, published as a conference paper at the 2024 Genetic and Evolutionary Computation Conference (GECCO '24) |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2312.11331 |
| رقم الأكسشن: | edsarx.2312.11331 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |