Special Session: Evolutionary Unsupervised Learning and Applications

Overview

Unsupervised learning – learning without labels – has seen a resurgence in popularity in the machine learning community in recent years due to its “human-like” learning style and ability to learn on data which has not been expertly labelled. This hot topic has seen an increasing research interest in the evolutionary computation community, but until now there has been no focus on unifying this in a special session.

In the world of “big data”, it is increasingly infeasible to label large datasets, or even to know the most suitable purpose for big data. Unsupervised learning is performed with no requirement of pre-existing knowledge, as part of exploratory data analysis. Unsupervised algorithms produce a range of invaluable knowledge: clustering automatically find related groups of data; dimensionality reduction simplifies data into a manageable and interpretable representation; visualisation techniques give a visual overview of data which can be easily understood by humans. There has been research into evolutionary approaches to these, and other unsupervised learning tasks, with promising results and a clear need for further, focused efforts by the evolutionary computation community.

Scope and Topics

The theme of this special session is the use of evolutionary computation in unsupervised learning algorithms and applications. ALL evolutionary computation paradigms will be considered for inclusion in this special session. The aim is to showcase new methods and theories in evolutionary unsupervised learning, and to explore the applicability of evolutionary unsupervised learning methods on a range of (real-world) applications. This includes fundamental research on unsupervised evolutionary methods, including unsupervised fitness functions, model representations, interpretability, and the theory of unsupervised learning in an evolutionary context; as well as a range of applications such as clustering, dimensionality reduction, manifold learning, visualisation, anomaly detection and others. Authors are invited to submit their original and unpublished/unsubmitted work to this special session.

Topics of interest include (but are not limited to):

Submission Guidelines

Please follow the submission guideline from the IEEE CEC 2021 Submission Website. Special session papers are treated the same as regular conference papers. Please specify that your paper is for the Special Session on Evolutionary Unsupervised Learning and Applications. All papers accepted and presented at CEC 2021 will be included in the conference proceedings published by IEEE Explore, which are typically indexed by EI.

Important Dates

Special Session Organizers

Dr Andrew Lensen, Victoria University of Wellington, New Zealand [email protected]

Dr Andrew Lensen is a Lecturer in Artificial Intelligence in the Evolutionary Computation Research Group in the School of Engineering and Computer Science at Victoria University of Wellington. He is leading the group’s research direction in unsupervised learning and has published on a range of evolutionary unsupervised learning topics including clustering, feature synthesis, unsupervised dimensionality reduction, and evolutionary manifold learning. He has over 15 papers published in leading peer-reviewed journals and conferences, nearly all of which are on unsupervised learning. His work on evolutionary manifold learning won Best Paper award at EuroGP 2019 and has been published in the IEEE Transactions on Cybernetics and Genetic Programming and Evolvable Machines.

Dr Lensen has served as a program committee member of many international conferences, including IEEE CEC, IEEE SSCI, IJCAI, GECCO, SEAL, and Australian AI. He serves as a regular reviewer for international journals such as the IEEE Transactions on Evolutionary Computation, and the IEEE Transactions on Cybernetics.

A/Prof Bing Xue, Victoria University of Wellington, New Zealand [email protected]

A/Prof Bing Xue is currently an Associate Professor and Program Director of Science in School of Engineering and Computer Science at VUW. She has over 200 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 neural networks, image analysis, transfer learning, multi-objective machine learning.

A/Prof Xue is currently the Chair of IEEE Computational Intelligence Society (CIS) Data Mining and Big Data Analytics Technical Committee, and Vice-Chair of IEEE Task Force on Evolutionary Feature Selection and Construction, Vice-Chair of IEEE CIS Task Force on Transfer Learning & Transfer Optimization, and of IEEE CIS Task Force on Evolutionary Deep Learning and Applications. She is an Associate Editor or Member of the Editorial Board for seven international journals, including IEEE Transactions of Evolutionary Computation, IEEE Computational Intelligence Magazine, and ACM Transactions on Evolutionary Learning and Optimisation.

A/Prof Xue is the organiser of the special session on Evolutionary Feature Selection and Construction in the IEEE Congresses on Evolutionary Computation (CEC) 2015–2020. A/Prof Xue has been a chair for a number of international conferences including the Chair of [email protected] 2018 and a co-Chair of the Evolutionary Machine Learning Track for GECCO 2019 and 2020. She is the Lead Chair of the IEEE Symposium on Computational Intelligence in Feature Analysis, Selection, and Learning in Image and Pattern Recognition (FASLIP) at SSCI 2016–2020, and Finance Chair for the 2019 IEEE Congress on Evolutionary Computation.

Prof Mengjie Zhang, Victoria University of Wellington, New Zealand [email protected]

Prof Mengjie Zhang is a Fellow of Royal Society of New Zealand, a Fellow of IEEE, and currently Professor of Computer Science at Victoria University of Wellington, where he heads the interdisciplinary Evolutionary Computation Research Group. He is a member of the University Academic Board, a member of the University Postgraduate Scholarships Committee, Associate Dean (Research and Innovation) in the Faculty of Engineering, and Chair of the Research Committee of the Faculty of Engineering and School of Engineering and Computer Science.

His research is mainly focused on evolutionary computation, particularly genetic programming, particle swarm optimisation and learning classifier systems with application areas of feature selection/construction and dimensionality reduction, computer vision and image processing, evolutionary deep learning and transfer learning, job shop scheduling, multi-objective optimisation, and clustering and classification with unbalanced and missing data. He is also interested in data mining, machine learning, and web information extraction. Prof Zhang has published over 500 research papers in refereed international journals and conferences in these areas.

He has been serving as an associated editor or editorial board member for over 10 international journals including IEEE Transactions on Evolutionary Computation, IEEE Transactions on Cybernetics, IEEE Transactions on Emergent Topics in Computational Intelligence, and as a reviewer of over 30 international journals. He has been a major chair for eight international conferences. He has also been serving as a steering committee member and a program committee member for over 80 international conferences including all major conferences in evolutionary computation. Since 2007, he has been listed as one of the top ten world genetic programming researchers by the GP bibliography (http://www.cs.bham.ac.uk/~wbl/biblio/gp-html/index.html).