2021 IEEE Congress on Evolutionary Computation, CEC 2021 - Proceedings, pages 926-933
Multi-Objective Evolutionary Design of Composite Data-Driven Models
Publication type: Proceedings Article
Publication date: 2021-06-28
—
Abstract
In this paper, a multi-objective approach for the design of composite data-driven mathematical models is proposed. It allows automating the identification of graph-based heterogeneous pipelines that consist of different blocks: machine learning models, data preprocessing blocks, etc. The implemented approach is based on a parameter-free genetic algorithm (GA) for model design called GPComp@Free. It is developed to be part of automated machine learning solutions and to increase the efficiency of the modeling pipeline automation. A set of experiments was conducted to verify the correctness and efficiency of the proposed approach and substantiate the selected solutions. The experimental results confirm that a multi-objective approach to the model design allows us to achieve better diversity and quality of obtained models. The implemented approach is available as a part of the open-source AutoML framework FEDOT.
Citations by journals
1
|
|
Procedia Computer Science
|
Procedia Computer Science
1 publication, 20%
|
Lecture Notes in Computer Science
|
Lecture Notes in Computer Science
1 publication, 20%
|
ACM Transactions on Evolutionary Learning and Optimization
|
ACM Transactions on Evolutionary Learning and Optimization
1 publication, 20%
|
ACS applied materials & interfaces
|
ACS applied materials & interfaces
1 publication, 20%
|
AI Communications
|
AI Communications
1 publication, 20%
|
1
|
Citations by publishers
1
|
|
Elsevier
|
Elsevier
1 publication, 20%
|
Springer Nature
|
Springer Nature
1 publication, 20%
|
Association for Computing Machinery (ACM)
|
Association for Computing Machinery (ACM)
1 publication, 20%
|
American Chemical Society (ACS)
|
American Chemical Society (ACS)
1 publication, 20%
|
IOS Press
|
IOS Press
1 publication, 20%
|
1
|
- We do not take into account publications that without a DOI.
- Statistics recalculated only for publications connected to researchers, organizations and labs registered on the platform.
- Statistics recalculated weekly.
{"yearsCitations":{"type":"bar","data":{"show":true,"labels":[2021,2022,2023],"ids":[0,0,0],"codes":[0,0,0],"imageUrls":["","",""],"datasets":[{"label":"Citations number","data":[1,1,3],"backgroundColor":["#3B82F6","#3B82F6","#3B82F6"],"percentage":["20","20","60"],"barThickness":null}]},"options":{"indexAxis":"x","maintainAspectRatio":true,"scales":{"y":{"ticks":{"precision":0,"autoSkip":false,"font":{"family":"Montserrat"},"color":"#000000"}},"x":{"ticks":{"stepSize":1,"precision":0,"font":{"family":"Montserrat"},"color":"#000000"}}},"plugins":{"legend":{"position":"top","labels":{"font":{"family":"Montserrat"},"color":"#000000"}},"title":{"display":true,"text":"Citations per year","font":{"size":24,"family":"Montserrat","weight":600},"color":"#000000"}}}},"journals":{"type":"bar","data":{"show":true,"labels":["Procedia Computer Science","Lecture Notes in Computer Science","ACM Transactions on Evolutionary Learning and Optimization","ACS applied materials & interfaces","AI Communications"],"ids":[14281,1022,34129,1458,16949],"codes":[0,0,0,0,0],"imageUrls":["\/storage\/images\/resized\/GDnYOu1UpMMfMMRV6Aqle4H0YLLsraeD9IP9qScG_medium.webp","\/storage\/images\/resized\/voXLqlsvTwv5p3iMQ8Dhs95nqB4AXOG7Taj7G4ra_medium.webp","\/storage\/images\/resized\/XZDD1UbkaHV0BImS1Dm7kQfvovjiljgbqNi7vyqK_medium.webp","\/storage\/images\/resized\/iLiQsFqFaSEx6chlGQ5fbAwF6VYU3WWa08hkss0g_medium.webp","\/storage\/images\/resized\/KqWdDIHwzps3KNMz2kSZBC4SgMnkL3bwEKJqtZ2u_medium.webp"],"datasets":[{"label":"","data":[1,1,1,1,1],"backgroundColor":["#3B82F6","#3B82F6","#3B82F6","#3B82F6","#3B82F6"],"percentage":[20,20,20,20,20],"barThickness":13}]},"options":{"indexAxis":"y","maintainAspectRatio":false,"scales":{"y":{"ticks":{"precision":0,"autoSkip":false,"font":{"family":"Montserrat"},"color":"#000000"}},"x":{"ticks":{"stepSize":null,"precision":0,"font":{"family":"Montserrat"},"color":"#000000"}}},"plugins":{"legend":{"position":"top","labels":{"font":{"family":"Montserrat"},"color":"#000000"}},"title":{"display":true,"text":"Journals","font":{"size":24,"family":"Montserrat","weight":600},"color":"#000000"}}}},"publishers":{"type":"bar","data":{"show":true,"labels":["Elsevier","Springer Nature","Association for Computing Machinery (ACM)","American Chemical Society (ACS)","IOS Press"],"ids":[17,8,1141,40,29],"codes":[0,0,0,0,0],"imageUrls":["\/storage\/images\/resized\/GDnYOu1UpMMfMMRV6Aqle4H0YLLsraeD9IP9qScG_medium.webp","\/storage\/images\/resized\/voXLqlsvTwv5p3iMQ8Dhs95nqB4AXOG7Taj7G4ra_medium.webp","\/storage\/images\/resized\/XZDD1UbkaHV0BImS1Dm7kQfvovjiljgbqNi7vyqK_medium.webp","\/storage\/images\/resized\/iLiQsFqFaSEx6chlGQ5fbAwF6VYU3WWa08hkss0g_medium.webp","\/storage\/images\/resized\/KqWdDIHwzps3KNMz2kSZBC4SgMnkL3bwEKJqtZ2u_medium.webp"],"datasets":[{"label":"","data":[1,1,1,1,1],"backgroundColor":["#3B82F6","#3B82F6","#3B82F6","#3B82F6","#3B82F6"],"percentage":[20,20,20,20,20],"barThickness":13}]},"options":{"indexAxis":"y","maintainAspectRatio":false,"scales":{"y":{"ticks":{"precision":0,"autoSkip":false,"font":{"family":"Montserrat"},"color":"#000000"}},"x":{"ticks":{"stepSize":null,"precision":0,"font":{"family":"Montserrat"},"color":"#000000"}}},"plugins":{"legend":{"position":"top","labels":{"font":{"family":"Montserrat"},"color":"#000000"}},"title":{"display":true,"text":"Publishers","font":{"size":24,"family":"Montserrat","weight":600},"color":"#000000"}}}}}
Metrics
Cite this
GOST |
RIS |
BibTex
Cite this
GOST
Copy
Polonskaia I. S. et al. Multi-Objective Evolutionary Design of Composite Data-Driven Models // 2021 IEEE Congress on Evolutionary Computation, CEC 2021 - Proceedings. 2021. pp. 926-933.
GOST all authors (up to 50)
Copy
Polonskaia I. S., Nikitin N. O., Revin I., Vychuzhanin P., Kalyuzhnaya A. Multi-Objective Evolutionary Design of Composite Data-Driven Models // 2021 IEEE Congress on Evolutionary Computation, CEC 2021 - Proceedings. 2021. pp. 926-933.
Cite this
RIS
Copy
TY - CPAPER
DO - 10.1109/CEC45853.2021.9504773
UR - https://doi.org/10.1109%2FCEC45853.2021.9504773
TI - Multi-Objective Evolutionary Design of Composite Data-Driven Models
T2 - 2021 IEEE Congress on Evolutionary Computation, CEC 2021 - Proceedings
AU - Polonskaia, Iana S
AU - Nikitin, Nikolay O
AU - Revin, Ilia
AU - Vychuzhanin, Pavel
AU - Kalyuzhnaya, Anna
PY - 2021
DA - 2021/06/28 00:00:00
SP - 926-933
ER -
Cite this
BibTex
Copy
@inproceedings{2021_Polonskaia
author = {Iana S Polonskaia and Nikolay O Nikitin and Ilia Revin and Pavel Vychuzhanin and Anna Kalyuzhnaya},
title = {Multi-Objective Evolutionary Design of Composite Data-Driven Models},
year = {2021},
pages = {926--933},
month = {jun}
}