In machine learning and data mining, the quality of the input data determines the quality of the output (e.g. accuracy), known as the GIGO (Garbage In, Garbage Out) principle. For a given problem, the input data of a learning algorithm is almost always expressed by a number of features (attributes or variables). Therefore, the quality of the feature space is a key for success of any machine learning and data algorithm.

Feature selection, feature extraction or construction and dimensionality reduction are important and necessary data pre-processing steps to increase the quality of the feature space, especially with the trend of big data. Feature selection aims to select a small subset of important features from the original feature set. Feature extraction or construction aims to create a set of effective, high-level features. Dimensionality reduction aims to reduce the data space dimensionality, mitigating the “curse of dimensionality.”

The theme of this special session is the use of evolutionary computation for feature reduction, covering all evolutionary computation paradigms. Authors are invited to submit original and unpublished work on theories, methods, and applications related to feature selection, extraction, and dimensionality reduction.

• Dimensionality reduction
• Feature ranking and weighting
• Feature subset selection
• Multi-objective feature selection
• Filter, wrapper, and embedded methods for feature selection
• Feature extraction or construction
• Single-feature or multiple-features construction
• Filter, wrapper, and embedded methods for feature extraction
• Multi-objective feature extraction
• Feature selection, extraction, and dimensionality reduction in image analysis, pattern recognition, classification, clustering, regression, and other tasks
• Feature selection, extraction, and dimensionality reduction on high-dimensional and large-scale data
• Analysis of evolutionary feature selection, extraction, and dimensionality reduction algorithms
• Hybridisation of evolutionary computation with neural networks and fuzzy systems for feature selection and extraction
• Hybridisation of evolutionary computation with machine learning, information theory, statistics, and mathematical modelling for feature selection and extraction
• Real-world applications of evolutionary feature selection and extraction, including image and video analysis, face recognition, gene analysis, biomarker detection, medical data classification and diagnosis, handwritten digit recognition, text mining, instrument recognition, power systems, financial data, and business analytics

• Paper Submission Deadline: January 31, 2026 (STRICT DEADLINE)
• Notification of Acceptance: March 15, 2026
• Final Paper Submission: April 15, 2026

Please follow the IEEE CEC 2026 Submission Website. Special session papers are treated the same as regular conference papers. Please specify that your paper is submitted to the special session on Evolutionary Computation for Feature Selection, Extraction and Dimensionality Reduction. All accepted papers will be published in IEEE Xplore.

The program committee includes leading researchers from around the world:

• Dr Bach Nguyen, Victoria University of Wellington, New Zealand — Hoai.Bach.Nguyen@ecs.vuw.ac.nz
• A/Prof Ruwang Jiao, Soochow University, China — rwjiao@suda.edu.cn
• Prof Bing Xue, Victoria University of Wellington, New Zealand — Bing.Xue@ecs.vuw.ac.nz

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Biographies

Bach Nguyen is currently a Lecturer in Artificial Intelligence with the School of Engineering and Computer Science at Te Herenga Waka — Victoria University of Wellington (VUW), New Zealand. He received his Ph.D. in Computer Science from the same university in 2018. His research interests include evolutionary computation, feature selection and construction, multi-objective optimisation, transfer learning, and multi-label learning. Dr Nguyen is currently the Chair of the IEEE New Zealand Central Section and the Vice-Chair of the IEEE Computational Intelligence Society Task Force on Evolutionary Computation for Feature Selection and Construction. He has chaired special sessions and symposia at major IEEE conferences including CEC (2021–2025) and SSCI (2021–2023). He is a Principal Investigator on an MBIE Smart Ideas grant (2024–2027) focused on evolutionary learning for multi-millennial sea-level modelling.

Ruwang Jiao is currently an Associate Professor with Soochow University, Suzhou, China. He was a Postdoctoral Research Fellow with the School of Engineering and Computer Science, Victoria University of Wellington, New Zealand. His research interests mainly include feature selection, dimensionality reduction, and evolutionary multi-objective learning. He received the Humboldt Research Fellowship and JSPS Research Fellowship in 2024. He is the chair of the IEEE CIS Task Force on Evolutionary Computation for Feature Selection and Construction. He Co-Chaired the special session on EMOML at IEEE CEC 2023 and IEEE WCCI 2024 and delivered a tutorial at IEEE SSCI 2023.

Bing Xue is currently a Professor of Artificial Intelligence, and Deputy Head of School in the School of Engineering and Computer Science at VUW. She has over 500 papers published in fully refereed international journals and conferences and her research focuses mainly on evolutionary computation, machine learning, classification, symbolic regression, feature selection, evolving deep NNs, image analysis, transfer learning, multi-objective machine learning. Prof. Xue is currently the Chair of IEEE CIS Evolutionary Computation Technical Committee. She has also served as associate editor of several international journals, such as IEEE CIM, IEEE TEVC and ACM TELO. She is also a Fellow of Engineering New Zealand.