GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, pages 43-44

Automatic evolutionary learning of composite models with knowledge enrichment

Kalyuzhnaya Anna V ¹

Nikitin Nikolay O ¹

Vychuzhanin Pavel ¹

Hvatov Alexander ¹

Boukhanovsky Alexander ¹

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ITMO university, Saint Petersburg, Russia |

Publication type: Proceedings Article

Publication date: 2020-07-08

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DOI: 10.1145/3377929.3398167

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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.

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Computers and Geosciences	Computers and Geosciences, 1, 16.67% Computers and Geosciences 1 publication, 16.67%
Future Generation Computer Systems	Future Generation Computer Systems, 1, 16.67% Future Generation Computer Systems 1 publication, 16.67%
Procedia Computer Science	Procedia Computer Science, 1, 16.67% Procedia Computer Science 1 publication, 16.67%
Communications in Computer and Information Science	Communications in Computer and Information Science, 1, 16.67% Communications in Computer and Information Science 1 publication, 16.67%
Applied Sciences (Switzerland)	Applied Sciences (Switzerland), 1, 16.67% Applied Sciences (Switzerland) 1 publication, 16.67%
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Elsevier	Elsevier, 3, 50% Elsevier 3 publications, 50%
Springer Nature	Springer Nature, 1, 16.67% Springer Nature 1 publication, 16.67%
Multidisciplinary Digital Publishing Institute (MDPI)	Multidisciplinary Digital Publishing Institute (MDPI), 1, 16.67% Multidisciplinary Digital Publishing Institute (MDPI) 1 publication, 16.67%
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Kalyuzhnaya A. V. et al. Automatic evolutionary learning of composite models with knowledge enrichment // GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion. 2020. pp. 43-44.

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Kalyuzhnaya A. V., Nikitin N. O., Vychuzhanin P., Hvatov A., Boukhanovsky A. Automatic evolutionary learning of composite models with knowledge enrichment // GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion. 2020. pp. 43-44.

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TY - CPAPER

DO - 10.1145/3377929.3398167

UR - https://doi.org/10.1145%2F3377929.3398167

TI - Automatic evolutionary learning of composite models with knowledge enrichment

T2 - GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion

AU - Kalyuzhnaya, Anna V

AU - Nikitin, Nikolay O

AU - Vychuzhanin, Pavel

AU - Hvatov, Alexander

AU - Boukhanovsky, Alexander

PY - 2020

DA - 2020/07/08 00:00:00

SP - 43-44

ER -

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@inproceedings{2020_Kalyuzhnaya,

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},

year = {2020},

pages = {43--44},

month = {jul}

}

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