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CFP: IEEE CEC 2025 - Special Session on Evolutionary Multi-Objective Automated Machine Learning (EMO-AutoML) [DEADLINE: January 15]

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Mustafa MISIR

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Dec 19, 2024, 6:14:18 AM12/19/24
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** Apologies for cross-posting **

Call for Papers

Special Session on EMO-AutoML: Evolutionary Multi-Objective Automated Machine Learning @ IEEE CEC 2025

Submission deadline:  January 15, 2025

For submissionhttps://www.cec2025.org/index/page.html?id=1298 

Scope: Automated Machine Learning (AutoML) optimizes machine learning tasks by automating feature processing, model selection, and parameter tuning, reducing reliance on human expertise. It can match or surpass manual tuning results, cutting costs and advancing Artificial Intelligence (AI) research. While early AutoML focused on single objectives, real-world applications often require balancing multiple objectives, such as accuracy, model complexity, and resource usage. Evolutionary Computation (EC) offers promising solutions for Multi-objective AutoML (MO-AutoML). 

The aim of this special session is to gather researchers studying EC for MO-AutoML (EMO-AutoML) to share their research on the following non-exhaustive list of topics:

- Advanced EC techniques on AutoML,
- EC for Multiobjective automated feature engineering, such as feature selection, and feature extraction,
- EC for Multiobjective automated Neural Architecture Search (NAS),
- EC for Multiobjective automated hyperparameter optimization,
- EC for Multiobjective automated data augmentation,
- EC for Multiobjective automated data cleaning,
- EC for Multiobjective model compression,
- EC for Multiobjective model combination,
- EC for Multiobjective ensemble learning,
- EMO-AutoML frameworks,
- EMO-AutoML for different learning tasks such as classification/regression, unsupervised, semi-supervised, self-supervised, few-shot, and transfer learning,
- Real-world applications with MO-AutoML.

Organizers:

Mustafa Misir, Duke Kunshan University, China,  mustaf...@dukekunshan.edu.cn
Zhongyi Hu, Wuhan University, China,  zhongyi.hu@whu.edu.cn
Yi Mei, Victoria University of Wellington, New Zealand,  yi.mei@ecs.vuw.ac.nz


-----------------------------

Best regards,

 

Mustafa MISIR

 

Assoc. Prof. of Data and Computational Science

Lead, Machine lEarning and Operations Research (MEmORy) Lab

Division of Natural and Applied Sciences

Duke Kunshan University, Office: WDR 2106

Duke Avenue No. 8, Kunshan, Jiangsu, China 215316  

 

Webhttp://mustafamisir.github.io  |  http://memoryrlab.github.io  

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