- author = "Bogdan Burlacu and Gabriel Kronberger and Michael Kommenda",
- title = "{Operon C++}: An Efficient Genetic Programming Framework for Symbolic Regression",
- 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 = "1562--1570",
- address = "internet",
- series = "GECCO '20",
- month = jul # " 8-12",
- organisation = "SIGEVO",
- keywords = "genetic algorithms, genetic programming, C++, symbolic regression",
- isbn13 = "9781450371278",
-
URL = "
https://doi.org/10.1145/3377929.3398099",
-
DOI = "
doi:10.1145/3377929.3398099",
-
code_url = "https://github.com/heal-research/operon",
- size = "9 pages",
-
abstract = "Genetic Programming (GP) is a dynamic field of
research where empirical testing plays an important
role in validating new ideas and algorithms. The
ability to easily prototype new algorithms by reusing
key components and quickly obtain results is therefore
important for the researcher.
In this paper we introduce Operon, a C++ GP framework focused on performance, modularity and usability, featuring an efficient linear tree encoding and a scalable concurrency model where each logical thread is responsible for generating a new individual. We model the GP evolutionary process around the concept of an offspring generator, a streaming operator that defines how new individuals are obtained. The approach allows different algorithmic variants to be expressed using high-level constructs within the same generational basic loop. The evaluation routine supports both scalar and dual numbers, making it possible to calculate model derivatives via automatic differentiation. This functionality allows seamless integration with gradient-based local search approaches.
We discuss the design choices behind the proposed framework and compare against two other popular GP frameworks, DEAP and HeuristicLab. We empirically show that Operon is competitive in terms of solution quality, while being able to generate results significantly faster.",
-
notes = "See also \cite{LaCava:2021:NeurIPS}
Also known as \cite{10.1145/3377929.3398099} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)",
