Genetic Programming Theory and Practice brings together some of the most impactful researchers in the field of genetic programming (GP), each one working on unique and interesting intersections of theoretical development and practical applications of this evolutionary-based machine learning paradigm. Topics of particular interest for this year's volume include powerful modeling techniques through GP-based symbolic regression, novel selection mechanisms that help guide the evolutionary process, modular approaches to GP, and applications in cybersecurity, biomedicine and program synthesis, as well as papers by practitioner of GP that focus on usability and real-world results. In summary, readers will get a glimpse of the current state-of-the-art in GP research.

Lies at the interdisciplinary boundary between cutting-edge research in Genetic Programming and practical applicability Explores the intersection of Evolutionary Computation and Machine Learning, Deep Learning methods and hybrid approaches Discusses GP, including novel selection strategies, modular architectures and unique fitness assignment strategies

Autorentext

Bogdan Burlacu is a lecturer of computer science at the Gheorghe Asachi Technical University of Iai, 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 20052011. 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.

Inhalt

Chapter 1. On the Effects of Continuous Pruning on Symbolic Regression for Different Variants of Evolutionary Search.- Chapter 2. Analyzing Fitness Aggregation Strategies for Symbolic Regression Problem-Solving.- Chapter 3. The Evolution of Heterogeneous Logic: An Analysis of the Buffet Method.- Chapter 4. FPGA-Based Streaming Processors for Tree-Based Genetic Programming.- Chapter 5. On Interpretability in Multimodal Biomedical Image Analysis.- Chapter 6. CANTS-GP: A Nature-Inspired Metaheuristic for Graph Based Genetic Programs.- Chapter 7. Bridging Genetic Programming and Type Theory Research.- Chapter 8. To Smoothly Go Where No Model has Gone Before: Pareto Tournaments, Model Curvature and Alternating Objectives.- Chapter 9. GP and LLMs for Program Synthesis: No ClearWinners.- Chapter 10. Offline reinforcement learning: A New Challenge for Symbolic Regression.- Chapter 11. Language Model-Driven Program Synthesis with Program Trace Optimization on the Abstraction and Reasoning Corpus.- Chapter 12. Evolving Programs in the Lambda Calculus using Program Trace Optimisation.- Chapter 13. Interpretable Control with Graph-based Genetic Programming.- Chapter 14. Decoupling Representation and Learning in Genetic Programming:the LaSER Approach.- Chapter 15. Tips on Effective Theory and Practice of Genetic Programming.- Chapter 16. Spatial Genetic Programming with the S1 Processing Board.- Chapter 17. Applications of Evolutionary Algorithms for Instrument Design.- Chapter 18. Agentic GP: A Theoretical Framework for the Development of Genetic Programming Systems via Agentic AI.- Chapter 19. Evolution of Artificial Intelligence, Continued.- Chapter 20. Heeding Good Advice: Scaling Down and Specializing in the Age of Big AI.- Chapter 21. The Gegelati Framework for Efficient and Reproducible Solutions with Tangled Program Graphs.