Genetic Programming Theory and Practice XXII

Bogdan Burlacu is a lecturer of computer science at the Gheorghe Asachi Technical University of Iași, Romania. He specializes in machine learning and symbolic regression, focusing on genetic programming evolutionary dynamics, and is co-author of the book “Symbolic Regression”. His research focuses on developing and refining methods that extract meaningful mathematical models directly from data, contributing to advances in both the theoretical foundations and practical applications of symbolic regression. He is also the author of the Operon symbolic regression library.

Fabrício Olivetti de França is a professor of computer science at the Federal University of ABC, current head of the Heuristics, Analysis and Learning Laboratory (HAL) and the coordinator of the graduate program of computer science at the same university. His work in symbolic regression comprehends the creation of new techniques promoting interpretability and the integration of domain knowledge. He is also one of the main contributors of SRBench having helped to host multiple competitions for symbolic regression and the current version of SRBench. He co-organized Symbolic Regression workshops at GECCO for the past years together with Gabriel Kronberger and William La Cava. He is also part of the organization of the Genetic Programming Theory and Practice workshop.

Alexander Lalejini is an assistant professor in the Department of Computer Science at Grand Valley State University and holds a dual PhD in Computer Science and Ecology, Evolution, and Behavior from Michigan State University. His research intersects computer science and evolution, applying the principles of each field to advance the other. Broadly, his work focuses on (1) developing digital systems to investigate fundamental questions about how evolution works, (2) harnessing our understanding of evolution to engineer new algorithms to solve challenging computational problems, and (3) facilitating knowledge transfer between the fields of evolutionary biology and evolutionary computing.

Stephen Kelly is an artist and assistant professor in the Department of Computing and Software at McMaster University. His computer science research investigates how emergent forms of memory and hierarchy allow digital evolution to build algorithms in dynamic, partially-observable, and multi-task temporal sequence prediction environments. His research-creation works are mechatronic art/science hybrids which use nature-inspired computing as raw material for storytelling, activism, and public engagement. He received his PhD in computer science from Dalhousie University, BFA from the Nova Scotia College of Art and Design, and completed an NSERC post-doctoral fellowship at the BEACON Center for the study of Evolution in Action at Michigan State University.

Wolfgang Banzhaf is the John R. Koza Chair for Genetic Programming in the Department of Computer Science and Engineering at Michigan State University. He received his Dr.rer.nat (PhD) from the Department of Physics of the Technische Hochschule Karlsruhe, now Karlsruhe Institute of Technology (KIT). His research interests are evolutionary computing, complex adaptive systems, and self-organization of artificial life. He is a member of the Advisory Committee of ACM-SIGEVO, the Special Interest Group for Evolutionary Computation of the Association of Computing Machinery and has served as its Chair from 2011 to 2015 after having served as SIGEVO’s treasurer 2005–2011. From its foundation, he was member of the Executive Board of SIGEVO from 2005 to 2021, and of the International Society for Artificial Life (ISAL) from 2009 to 2015, and from 2019 to today. He has founded the scholarly journal “Genetic Programming and Evolvable Machines”.