Automated evolutionary approach for the design of composite machine learning pipelines

Nikitin N.O., Vychuzhanin P., Sarafanov M., Polonskaia I.S., Revin I., Barabanova I.V., Maximov G., Kalyuzhnaya A.V., Boukhanovsky A.
Тип документаJournal Article
Дата публикации2022-02-01
Название журналаFuture Generation Computer Systems
Квартиль по SCImagoQ1
Квартиль по Web of ScienceQ1
Импакт-фактор 20217.31
Hardware and Architecture
Computer Networks and Communications
Пристатейные ссылки: 73
Цитируется в публикациях: 6
AutoML: A Survey of the State-of-the-Art
He X., Zhao K., Chu X.
Q1 Knowledge-Based Systems 2021 цитирований: 163
Towards generative design of computationally efficient mathematical models with evolutionary learning
Kalyuzhnaya A.V., Nikitin N.O., Hvatov A., Maslyaev M., Yachmenkov M., Boukhanovsky A.
Q2 Entropy 2021 цитирований: 7
Open Access
Open access
A practical tutorial on bagging and boosting based ensembles for machine learning: Algorithms, software tools, performance study, practical perspectives and opportunities
González S., García S., Del Ser J., Rokach L., Herrera F.
Q1 Information Fusion 2020 цитирований: 57
Machine Learning Pipelines: From Research to Production
Posoldova A.
Q3 IEEE Potentials 2020 цитирований: 1
DeepLine: AutoML Tool for Pipelines Generation using Deep Reinforcement Learning and Hierarchical Actions Filtering
Heffetz Y., Vainshtein R., Katz G., Rokach L.
2020 цитирований: 3
Identifying Sepsis Subphenotypes via Time-Aware Multi-Modal Auto-Encoder
Yin C., Liu R., Zhang D., Zhang P.
2020 цитирований: 10
Automatic evolutionary learning of composite models with knowledge enrichment
Kalyuzhnaya A.V., Nikitin N.O., Vychuzhanin P., Hvatov A., Boukhanovsky A.
GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion 2020 цитирований: 6
A comprehensive hybrid first principles/machine learning modeling framework for complex industrial processes
Sun B., Yang C., Wang Y., Gui W., Craig I., Olivier L.
Q1 Journal of Process Control 2020 цитирований: 35
Structural Evolutionary Learning for Composite Classification Models
Nikitin N.O., Polonskaia I.S., Vychuzhanin P., Barabanova I.V., Kalyuzhnaya A.V.
Q2 Procedia Computer Science 2020 цитирований: 9
Open Access
Open access
GPML: an XML-based standard for the interchange of genetic programming trees
Dou T., Kaszubowski Lopes Y., Rockett P., Hathway E.A., Saber E.
Q2 Genetic Programming and Evolvable Machines 2019 цитирований: 1
Hybrid physics‐based and data‐driven modeling for bioprocess online simulation and optimization
Zhang D., Del Rio‐Chanona E.A., Petsagkourakis P., Wagner J.
Q1 Biotechnology and Bioengineering 2019 цитирований: 33
Auto-Keras: An Efficient Neural Architecture Search System
Jin H., Song Q., Hu X.
2019 цитирований: 192
MnasNet: Platform-Aware Neural Architecture Search for Mobile
Tan M., Chen B., Pang R., Vasudevan V., Sandler M., Howard A., Le Q.V.
2019 цитирований: 576
Ensemble learning: A survey
Sagi O., Rokach L.
Q1 Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 2018 цитирований: 382
Forecasting at Scale
Taylor S.J., Letham B.
Q1 American Statistician 2018 цитирований: 461
1. Nikitin N. O. и др. Automated evolutionary approach for the design of composite machine learning pipelines // Future Generation Computer Systems. 2022. Т. 127. С. 109–125.


DO - 10.1016/j.future.2021.08.022

UR -

TI - Automated evolutionary approach for the design of composite machine learning pipelines

T2 - Future Generation Computer Systems

AU - Nikitin, Nikolay O.

AU - Vychuzhanin, Pavel

AU - Sarafanov, Mikhail

AU - Polonskaia, Iana S.

AU - Revin, Ilia

AU - Barabanova, Irina V.

AU - Maximov, Gleb

AU - Kalyuzhnaya, Anna V.

AU - Boukhanovsky, Alexander

PY - 2022

DA - 2022/02

PB - Elsevier BV

SP - 109-125

VL - 127

SN - 0167-739X

ER -

BibTex |


doi = {10.1016/j.future.2021.08.022},

url = {},

year = 2022,

month = {feb},

publisher = {Elsevier {BV}},

volume = {127},

pages = {109--125},

author = {Nikolay O. Nikitin and Pavel Vychuzhanin and Mikhail Sarafanov and Iana S. Polonskaia and Ilia Revin and Irina V. Barabanova and Gleb Maximov and Anna V. Kalyuzhnaya and Alexander Boukhanovsky},

title = {Automated evolutionary approach for the design of composite machine learning pipelines},

journal = {Future Generation Computer Systems}


Nikitin, Nikolay O., et al. “Automated Evolutionary Approach for the Design of Composite Machine Learning Pipelines.” Future Generation Computer Systems, vol. 127, Feb. 2022, pp. 109–25. Crossref,