Special Session on Evolutionary Computations for Big Data Mining


IEEE Congress on Evolutionary Computation

Kraków, Poland


    In recent years a massive growth in the scale of data is observed. On one hand, big data has become one of the major topics for researchers and high-tech companies. On the other, thanks to the current advances in big data mining more and more organizations can get access to valuable insights and results. Methods and tools for big data analytics continue to form a lively research area. Mining big data requires not only huge computational resources but also proper methods and tools able to deal with the computational complexity of the knowledge discovery tasks. Typically, mining big data requires solving various optimization tasks like, for example, data cleaning, instance and feature reduction, setting structure, and parameters of learners used. Most of the above tasks are inherently computationally hard. The remaining are also difficult due to the sheer size of the datasets involved. To deal with such problems numerous approaches based on various heuristics and metaheuristics are currently studied. Among the most successful are methods based on evolutionary computations. Their advantage is the ability to obtain high-quality solutions to difficult problems. Their weakness is a high demand for computational resources and the necessity to carefully craft models to particular problems at hand. Nevertheless, analytical methods and tools, including data mining ones, based on the evolutionary computations paradigm, become more and more effective and widely used. This does not mean that the area does not require further research efforts. On the contrary, the ever-growing size of data that needs to be mined coupled with advances in the field of computation theory and technology, plus efforts to improve efficiency, explainability, interpretability, and reliability of evolutionary computations, pose serious and open challenges to the evolutionary computations research community.

    This special session is open for presenting different methods, tools, and applications for mining big data using broadly understood evolutionary computations paradigm. Especially welcomed are papers presenting advances in the field of constructing or improving algorithms inspired by biological evolution that can be used for mining big data. The session solicits also papers presenting practical applications of the big data mining tools supported by evolutionary computations techniques.


    Topics of interest of the proposed session include, but are not limited to, the use of the evolutionary computation methods in:

    • Data preprocessing
    • Dimensionality reduction
    • Imbalanced learning
    • Data cleaning
    • Constructing novel learners
    • Parallel and distributed computations for data preprocessing and analysis
    • Application of intelligent techniques in data science
    • Other related topics

    The session aims at addressing such issues from practical and theoretical perspectives.


    The submitted papers should present results of the original and unpublished research. The papers will be reviewed by the International Program Committee. Special session papers are treated the same as regular conference papers. All papers accepted and presented at CEC 2021 will be included in the conference proceedings published by IEEE Explore.

    Please follow the submission guideline from the IEEE CEC 2021 Submission Website and specify that your paper is for the Special Session on Evolutionary Computations for Big Data Mining.


    • 31 Jan 2021 21 February 2021 : Paper Submission Deadline
    • 22 Mar 2021 6 April 2021 : Paper Acceptance Notification
    • 7 Apr 2021 23 April 2021 : Final Paper Submission & Early Registration Deadline
    • 28 June 2021 - 1 July 2021: Conference Date

    More details is available at the IEEE CEC 2021 website.


    Ireneusz Czarnowski, Gdynia Maritime University, Poland - i.czarnowski@umg.edu.pl

    IRENEUSZ CZARNOWSKI is a graduate of the Faculty of Electrical Engineering at Gdynia Maritime University. He gained a doctoral degree in the field of computer science at Poznan University of Technology and a postdoctoral degree in the field of computer science at Wroclaw University of Science and Technology. Since 1998 is associated with Gdynia Maritime University, currently is a professor of computer science in the Department of Information Systems, Gdynia Maritime University. His main research interests are related to the use of artificial intelligence methods in decision support systems and data mining. His research activity is concerned with machine learning, data reduction for machine learning, and data mining. His scientific work also focuses on research in the field of optimization methods and applications of agent systems. He has published over 100 papers internationally. He is also a member of IEEE and, since 2016, he is the Secretary of the Polish branch of IEEE System, Man and Cybernetics.

    Piotr Jędrzejowicz, Gdynia Maritime University, Poland - p.jedrzejowicz@umg.edu.pl

    PIOTR JĘDRZEJOWICZ, Ph.D., Dr. Habil. is a professor of computer science and Chair, Department of Information Systems, Gdynia Maritime University, Poland. His research interests include artificial intelligence and decision support systems. Prof. Jedrzejowicz has published 4 books and over 200 papers internationally. He is a member of the Computer Science Committee, Polish Academy of Science. During his career, Prof. Jędrzejowicz has been a visiting professor in Germany, Great Britain, China, Sweden, and a research fellow at the School of Computer Science, McGill University, Montreal. Between 2012 and 2016 he was serving as the Rector of the Gdynia Maritime University and between 2008 and 2012 as the Vice-Rector, Research.

    This special session is organised under umbrella of the Polish IEEE SMC Chapter.