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.

Session 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, in both theoretical and practical aspects, oriented on future generation computing.

Scope

Topics include but are not limited to the following:

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 rule based systems, machine learning and statistical methods.
Accelerated applications to 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, and other domains, etc.

Author submission information

During submission of your paper follow author instruction on Submission | IEEE WCCI 2024 website. Please specify in the system that your paper is intended for Special Session on Accelerated Evolutionary Algorithms for Deep Learning and Parallel Models in Future Generation Computing.

Locations

30.06 – 05.07 2024, Yokohama, Japan

28.06 - 01.07 2021, Kraków, Poland

Marcin Woźniak

Full Prof., D.Sc., Ph.D., Eng. is with the Faculty of Applied Mathematics, Silesian University of Technology, Poland.

Marcin Woźniak received the M.Sc. degree in applied mathematics, the Ph.D. degree in computational intelligence, the D.Sc. degree in computational intelligence and Full Professor honours from the President of Poland. M. Wozniak is currently a Full Professor with 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, Participant and Advisor) at Polish, Italian and Lithuanian universities and projects with applied results at IT industry both funded from the National Centre for Research and Development and abroad. He was a Visiting Researcher with universities in Italy, Sweden, and Germany.

He has authored/coauthored over 200 research papers in international conferences and journals. His current research interests include neural networks and fuzzy logic control systems with their applications together with various aspects of applied computational intelligence accelerated by evolutionary computation and federated learning models.

In 2017 he was awarded by the Polish Ministry of Science and Higher Education with a scholarship for an outstanding young scientist and in 2021 he received award from the Polish Ministry of Science and Higher Education for research achievements. From 2020 to 2023, each year Prof. M. Woźniak was presented among "TOP 2% Scientists in the World" by Stanford University for his career achievements.

Prof. Woźniak was the Editorial Board member or an Editor for Sensors, Machine Learning with Applications, Pattern Analysis and Applications, IEEE ACCESS, Measurement, Sustainable Energy Technologies and Assessments, Frontiers in Human Neuroscience, PeerJ CS, International Journal of Distributed Sensor Networks, Computational Intelligence and Neuroscience, Journal of Universal Computer Science, etc., and 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.

He is the author of 3 books and 150+ research papers, was General Co-Chair of the 2021 Congress on Evolutionary Computation (Krakow, Poland) and organizer and Chair of the annual IEEE SSCI Symposium on Computational Intelligence for Human-like Intelligence 2013-2021. He serves/served as Associate Editor of the IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS and the IEEE TRANSACTIONS ON COMPUTATIONAL INTELLIGENCE AND AI IN GAMES, and is founding chair of the IEEE ETTC Task Force on Towards Human-like Intelligence. His research interests include application of CI and AI methods to games, dynamic and bi-level optimization problems, human-machine co-learning and cooperation in problem solving. 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