Automatic Evolutionary Learning of Composite Models with Knowledge Enrichment

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Kalyuzhnaya:2020:GECCOcomp,

• author = "Anna V. Kalyuzhnaya and Nikolay O. Nikitin and Pavel Vychuzhanin and Alexander Hvatov and Alexander Boukhanovsky",
• title = "Automatic Evolutionary Learning of Composite Models with Knowledge Enrichment",
• booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion",
• 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",
• pages = "43--44",
• address = "internet",
• series = "GECCO '20",
• month = jul # " 8-12",
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, machine learning, AutoML, evolutionary learning, domain knowledge",
• isbn13 = "9781450371278",
• URL = "http://www.human-competitive.org/sites/default/files/nikitin_human_competitive.txt",
• URL = "http://www.human-competitive.org/sites/default/files/automatic_evolutionary_learning_of_composite_models_with_knowledge_enrichment.pdf",
• URL = "https://doi.org/10.1145/3377929.3398167",
• DOI = "doi:10.1145/3377929.3398167",
• video_url = "http://www.human-competitive.org/sites/default/files/hummies-nikitin-fedot-automl.mp4",
• video_url = "https://youtu.be/8lj9GSmoqqE",
• code_url = "https://github.com/ITMO-NSS-team/FEDOT.Algs",
• size = "2 pages",
• abstract = "This paper provides the main concepts of the knowledge-enriched AutoML approach and shortly describes the current results of the proof of concept implementation within the FEDOT framework. By knowledge enrichment, we mean the insertion of domain-specific models and expert-like meta-heuristics. Also, we involve multi-scale learning as a part of complex models identification. The proposed concepts make it possible to create effective and interpretable composite models.",
• notes = "Entered 2021 HUMIES see also \cite{Nikitin:2020:PCS}

  FEDOT: Evolutionary composite AutoML as Human-Competitive result. Human built ML pipeline 1:11 FEDOT = factory of automated ML architectures. GP 2:27 Pareto. Nice animations. human-like explainability 3:32. Met-ocean predictions, river flood forecasting, oil and gas, robotics, finance etc. 4:05 predict height of arctic ocean 4:25 flood forecasting hackathon first place https://news.itmo.ru/en/university_live/achievements/news/10409 against human teams? 4:44 Predict the quality of oil 5:02 human competitive result. FEDOT pipeline can be explained via sensitivity analysis 6:14 Easy to use. Python. Open source. nnikitin@itmo.ru 6:54

  ITMO University Saint Petersburg, Russia

  Also known as \cite{10.1145/3377929.3398167} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Anna V Kalyuzhnaya Nikolay O Nikitin Pavel Vychuzhanin Alexander A Hvatov Alexander Boukhanovsky

Citations