- author = "Alexander Hvatov and Mikhail Maslyaev",
- title = "The Data-Driven Physical-Based Equations Discovery Using Evolutionary Approach",
- year = "2020",
- editor = "Richard Allmendinger and Hugo Terashima Marin and Efren Mezura Montes and Thomas Bartz-Beielstein and Bogdan Filipic and Ke Tang and David Howard and Emma Hart and Gusz Eiben and Tome Eftimov and William {La Cava} and Boris Naujoks and Pietro Oliveto and Vanessa Volz and Thomas Weise and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Rui Wang and Ran Cheng and Guohua Wu and Miqing Li and Hisao Ishibuchi and Jonathan Fieldsend and Ozgur Akman and Khulood Alyahya and Juergen Branke and John R. Woodward and Daniel R. Tauritz and Marco Baioletti and Josu Ceberio Uribe and John McCall and Alfredo Milani and Stefan Wagner and Michael Affenzeller and Bradley Alexander and Alexander (Sandy) Brownlee and Saemundur O. Haraldsson and Markus Wagner and Nayat Sanchez-Pi and Luis Marti and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and Matthew Johns and Nick Ross and Ed Keedwell and Herman Mahmoud and David Walker and Anthony Stein and Masaya Nakata and David Paetzel and Neil Vaughan and Stephen Smith and Stefano Cagnoni and Robert M. Patton and Ivanoe {De Falco} and Antonio {Della Cioppa} and Umberto Scafuri and Ernesto Tarantino and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Erik Hemberg and Riyad Alshammari and Tokunbo Makanju and Fuijimino-shi and Ivan Zelinka and Swagatam Das and Ponnuthurai Nagaratnam and Roman Senkerik",
- publisher = "Association for Computing Machinery",
- publisher_address = "New York, NY, USA",
- booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion",
- pages = "129--130",
- address = "internet",
- series = "GECCO '20",
- month = jul # " 8-12",
- organisation = "SIGEVO",
- keywords = "genetic algorithms, genetic programming, data-driven models, generic programming, PDE discovery, equation discovery, sparse regression",
- isbn13 = "9781450371278",
-
URL = "
http://www.human-competitive.org/sites/default/files/entry_0.txt",
-
URL = "
http://www.human-competitive.org/sites/default/files/gecco2020final.pdf",
-
URL = "https://arxiv.org/abs/2004.01680",
-
URL = "https://doi.org/10.1145/3377929.3389943",
-
DOI = "
doi:10.1145/3377929.3389943",
- size = "2 pages",
-
abstract = "The modern machine learning methods allow one to
obtain the data-driven models in various ways. However,
the more complex the model is, the harder it is to
interpret. In the paper, we describe the algorithm for
the mathematical equations discovery from the given
observations data. The algorithm combines genetic
programming with the sparse regression.
This algorithm allows obtaining different forms of the resulting models. As an example, it could be used for governing analytical equation discovery as well as for partial differential equations (PDE) discovery.
The main idea is to collect a bag of the building blocks (it may be simple functions or their derivatives of arbitrary order) and consequently take them from the bag to create combinations, which will represent terms of the final equation. The selected terms pass to the evolutionary algorithm, which is used to evolve the selection. The evolutionary steps are combined with the sparse regression to pick only the significant terms. As a result, we obtain a short and interpretable expression that describes the physical process that lies beyond the data.
In the paper, two examples of the algorithm application are described: the PDE discovery for the metocean processes and the function discovery for the acoustics.",
-
notes = "Entered 2021 HUMIES
Also known as \cite{10.1145/3377929.3389943} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)",
