Szczegóły publikacji
Opis bibliograficzny
Where are we now? : a large benchmark study of recent symbolic regression methods / Patryk Orzechowski, William La Cava, Jason H. Moore // W: GECCO 2018 [Dokument elektroniczny] : the Genetic and Evolutionary Computation Conference : a recombination of the 27th International Conference on Genetic Algorithms (ICGA) and the 23rd Annual Genetic Programming Conference (GP) : July 15th–19th 2018, Kyoto, Japan. — Wersja do Windows. — Dane tekstowe. — USA : ACM, cop. 2018. — Dod. ISBN: 978-1-4503-5764-7. — e-ISBN: 978-1-4503-5618-3. — S. 1183–1190. — Wymagania systemowe: Adobe Reader. — Tryb dostępu: http://delivery.acm.org/10.1145/3210000/3205539/p1183-orzecho... [2019-09-18]. — Bibliogr. s. 1190, Abstr. — P. Orzechowski – afiliacja: University of Pennsylvania
Autorzy (3)
- Orzechowski Patryk
- La Cava William
- Moore Jason H.
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 124338 |
|---|---|
| Data dodania do BaDAP | 2019-10-09 |
| DOI | 10.1145/3205455.3205539 |
| Rok publikacji | 2018 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Association for Computing Machinery (ACM) |
| Konferencja | Genetic and Evolutionary Computations 2018 |
Abstract
In this paper we provide a broad benchmarking of recent genetic programming approaches to symbolic regression in the context of state of the art machine learning approaches. We use a set of nearly 100 regression benchmark problems culled from open source repositories across the web. We conduct a rigorous benchmarking of four recent symbolic regression approaches as well as nine machine learning approaches from scikit-learn. The results suggest that symbolic regression performs strongly compared to state-of-the-art gradient boosting algorithms, although in terms of running times is among the slowest of the available methodologies. We discuss the results in detail and point to future research directions that may allow symbolic regression to gain wider adoption in the machine learning community. © 2018 Copyright held by the owner/author(s).
