- author = "T. Nathan Mundhenk and Mikel Landajuela and Ruben Glatt and Claudio P. Santiago and Daniel M. Faissol and Brenden K. Petersen",
- title = "Symbolic Regression via Neural-Guided Genetic Programming Population Seeding",
- title2 = "Symbolic Regression via Deep Reinforcement Learning Enhanced Genetic Programming Seeding",
- booktitle = "Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems, NeurIPS 2021",
- editor = "Marc'Aurelio Ranzato and Alina Beygelzimer and Yann N. Dauphin and Percy Liang and Jennifer Wortman Vaughan",
- year = "2021",
- pages = "24912--24923",
- month = dec # " 6-14",
- address = "virtual",
- keywords = "genetic algorithms, genetic programming, Python",
- timestamp = "Tue, 03 May 2022 01:00:00 +0200",
-
biburl = "
https://dblp.org/rec/conf/nips/MundhenkLGSFP21.bib",
- bibsource = "dblp computer science bibliography, https://dblp.org",
-
URL = "https://proceedings.neurips.cc/paper/2021/hash/d073bb8d0c47f317dd39de9c9f004e9d-Abstract.html",
-
URL = "
https://proceedings.neurips.cc/paper_files/paper/2021/file/d073bb8d0c47f317dd39de9c9f004e9d-Paper.pdf",
-
code_url = "https://github.com/brendenpetersen/deep-symbolic-optimization",
- size = "12 pages",
- abstract = "Symbolic regression is the process of identifying mathematical expressions that fit observed output from a black-box process. It is a discrete optimization problem generally believed to be NP-hard. Prior approaches to solving the problem include neural-guided search (e.g. using reinforcement learning) and genetic programming. In this work, we introduce a hybrid neural-guided/genetic programming approach to symbolic regression and other combinatorial optimization problems. We propose a neural-guided component used to seed the starting population of a random restart genetic programming component, gradually learning better starting populations. On a number of common benchmark tasks to recover underlying expressions from a dataset, our method recovers 65percent more expressions than a recently published top-performing model using the same experimental setup. We demonstrate that running many genetic programming generations without interdependence on the neural-guided component performs better for symbolic regression than alternative formulations where the two are more strongly coupled. Finally, we introduce a new set of 22 symbolic regression benchmark problems with increased difficulty over existing benchmarks. Source code is provided at www.github.com/brendenpetersen/deep-symbolic-optimization.",
-
notes = "May 2023 Using title in -Paper.pdf rather than
proceedings.neurips.cc web pages
for ArXiv see \cite{DBLP:journals/corr/abs-2111-00053}.
Computational Engineering Division, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA",
