Adam Viktorin is an AI Researcher at the A.I.Lab, Department of Informatics and Artificial Intelligence, Tomas Bata University in Zlín (https://ailab.fai.utb.cz/). His current focus is generative AI—especially LLM-driven automated design, evaluation-in-the-loop workflows, and AutoML. He is the principal architect of EASE (Effortless Algorithmic Solution Evolution), an open, modular framework that automates the creation and refinement of solutions—algorithms, code, text, and images—using LLMs and other generators. Beyond GenAI, his research advances metaheuristics, with an emphasis on adaptive strategies and parameter control in Differential Evolution, benchmarking, and applications to real-world optimization tasks. He received his Ph.D. in 2021 from Tomas Bata University in Zlín with the thesis Control Parameter Adaptation in Differential Evolution. His broader interests span machine learning, data science, and interdisciplinary applications of soft computing.

Roman Senkerik is a Head of A.I.Lab, Department of Informatics and Artificial Intelligence, Tomas Bata University in Zlín (https://ailab.fai.utb.cz/).

His current focus is generative AI—especially LLM-driven automated design, evaluation-in-the-loop workflows, and AutoML. He is the co-architect of EASE (Effortless Algorithmic Solution Evolution), an open, modular framework that automates the creation and refinement of solutions—algorithms, code, text, and images—using LLMs and other generators. Beyond GenAI, his research advances metaheuristics, with an emphasis on adaptive strategies and parameter control in Differential Evolution, benchmarking, and applications to real-world optimization tasks. Prof. Roman Senkerik has made significant contributions to the fields of evolutionary computation and applications of artificial intelligence.

He is the author of over 50 journal papers, 250 conference papers, and several book chapters, as well as editorial notes. He is a recognized reviewer for many leading journals in computer science/computational intelligence. He was a part of the organizing teams for tutorials, special sessions, workshops, or symposiums at GECCO, IEEE WCCI, CEC, or SSCI events.

Michal Pluhacek is the ARTIQ project leader and professor at the AGH University of Krakow. His research interests include diverse branches of artificial intelligence, e.g. evolutionary computation, swarm intelligence, and, more recently, the applications of large language models. He has extensive international experience and numerous publications at world-leading congresses, conferences, and in respected journals. Michal Pluhacek received his Ph.D. degree in Information Technologies in 2016. His dissertation topic was “Modern Method of Development and Modifications of Evolutionary Computational Techniques.” Later, he was awarded the permanent associate. prof. title in 2023.

Niki van Stein is an Associate Professor at the Leiden Institute of Advanced Computer Science (LIACS), Leiden University, specializing in Explainable Artificial Intelligence (XAI). Since January 2022, Dr. van Stein has led the XAI research group and is a member of the management team of the Natural Computing cluster. Her research focuses on the intersection of machine learning, LLMs, optimization, and XAI, with applications in predictive maintenance, time-series analysis, and engineering design. Dr. van Stein obtained a PhD in Computer Science from Leiden University in 2018, under the supervision of Prof. Dr. Thomas Bäck, with a thesis on data-driven modelling and optimization of industrial processes.
With over 90 peer-reviewed publications and multiple awards, including best paper recognitions at GECCO and the IEEE Symposium Series on Computational Intelligence, Dr. van Stein has made significant contributions to the fields of evolutionary computing and explainable artificial intelligence.

Thomas Back received his Diploma and Ph.D. degrees in Computer Science from the University of Dortmund, Germany, in 1990 and 1994, respectively. He is a Professor of Computer Science at the Leiden Institute of Advanced Computer Science (LIACS), Leiden University, Netherlands, with research interests in evolutionary computation, machine learning, and their applications in sustainable industry and healthcare. He is a member of the Royal Netherlands Academy of Arts and Sciences (KNAW, 2021), IEEE Fellow (2022), and Academia Europaea (2022). He has received several awards, including the IEEE CIS Evolutionary Computation Pioneer Award (2015) and the best Ph.D. thesis award from the German Computer Science Society (1995). Dr. Bäck serves as Editor in Chief of the Evolutionary Computation Journal, and holds editorial roles with several other journals. He has co-edited major handbooks and authored notable works in evolutionary computation.

Lars Kotthoff is currently on sabbatical and serves as a visiting professor at Sorbonne University. Previously, he held the position of Derecho Professor and was a presidential faculty fellow in the Department of Electrical Engineering and Computer Science at the School of Computing, University of Wyoming. His research focuses on combining artificial intelligence and machine learning to create robust systems with state-of-the-art performance. He develops techniques to model the behavior of algorithms that solve computationally challenging problems in practice. These models help in selecting the most effective algorithm and determining the optimal parameter configuration for tackling specific problems. Additionally, he leads the Meta-Algorithmics, Learning, and Large-scale Empirical Testing (MALLET) lab and directs the Artificially Intelligent Manufacturing (AIM) center at the University of Wyoming.