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IEEE Congress on Evolutionary Computation (IEEE CEC)

Special Session:
Accelerated Evolutionary Algorithms for Deep Learning and Parallel Models in Future Generation Computing


Co-chairs:


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Marcin Woźniak

Faculty of Applied Mathematics, Silesian University of Technology

Gliwice, Poland

Marcin.Wozniak@polsl.pl


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Jacek Mańdziuk

Faculty of Mathematics and Information Science, Warsaw University of Technology

Warsaw, Poland

mandziuk@mini.pw.edu.pl



Accelerated evolutionary algorithms for deep learning and parallel models in future generation computing


Evolutionary computing is constantly growing in new aspects, both theoretical and practical, important for various applications in modern technology and industry. One of the recent trends is development of accelerated evolutionary algorithms which combine robustness of evolutionary methods with machine learning techniques. The main goal of this special session is to promote and advance research activities related to all facets of accelerated evolutionary algorithms.


Symposium organizers welcome high-quality original submissions related to accelerated evolutionary algorithms in combination with deep learning, reinforcement learning, autonomous learning, transfer learning or in hybrid constructions with other Artificial Intelligence solutions like neural networks, fuzzy and rule based systems both in theoretical and practical aspects oriented on future generation computing.


Scope

The scope of the session includes, but is not limited to:
  • Accelerated evolutionary, heuristics and bio inspired algorithms.
  • Parallelization of evolutionary, heuristic and bio-inspired algorithms for future generation computing.
  • Accelerated large-scale, multi-scale, multi-objective, constrained, sparse and free optimization.
  • Evolutionary based acceleration of training and data processing in deep learning, reinforcement learning, transfer learning.
  • Evolutionary based acceleration of neural networks, fuzzy logic and rue based systems, machine learning and statistical methods.
  • Accelerated applications in image processing, pattern recognition, expert systems, engineering problems, games, data mining and optimization for industry, finance, transport, logistics, economy, manufacturing, security, IoT, VR, robotics, healthcare, science, etc.

Locations

28.06 - 01.07 2021, Kraków, Poland

Marcin Woźniak

Ph.D., D.Sc., is Assoc. Prof. at the Faculty of Applied Mathematics, Silesian University of Technology.

He is a Scientific Supervisor in editions of “The Diamond Grant” and “The Best of the Best” programs for highly talented students from the Polish Ministry of Science and Higher Education. He participated in various scientific projects (as Lead Investigator, Scientific Investigator, Manager, or Participant) at Polish and Italian universities. He was a Visiting Researcher with universities in Italy, Sweden, and Germany. He has authored/coauthored more than 150 research papers in international conferences and journals. His current research interests include evolutionary algorithms and neural networks with their applications together with various aspects of theoretical and applied computational intelligence.

M. Woźniak was the Editorial Board member or an Editor for Sensors, IEEE ACCESS, Frontiers in Human Neuroscience, PeerJ CS, International Journal of Distributed Sensor Networks, Computational Intelligence and Neuroscience, Journal of Universal Computer Science, etc., and as a Session Chair at various international conferences and symposiums , including IEEE Symposium Series on Computational Intelligence, IEEE Congress on Evolutionary Computation, etc. For more information please visit https://orcid.org/0000-0002-9073-5347


Jacek Mańdziuk

Ph.D., D.Sc., is Full Professor at the Faculty of Mathematics and Information Science, Warsaw University of Technology (Warsaw / Poland), Head of Division of Artificial Intelligence and Computational Methods, and Head of Doctoral Programme in Computer Science, at this faculty.

He is the author of 3 books (including Knowledge-free and Learning-based Methods in Intelligent Game Playing, Springer, 2010) and 140+ research papers.

His research interests include application of Computational Intelligence and Artificial Intelligence methods to games, dynamic optimization problems, human-machine cooperation and financial modeling. He is also interested in development of general-purpose human-like learning and problem-solving methods which involve intuition, creativity and multitasking.

For more information please visit http://www.mini.pw.edu.pl/~mandziuk