Genetic Programming

The 26th European Conference on Genetic Programming (EuroGP 2023) took place at
the Brno University of Technology (BUT), Brno, Czech Republic, April 12-14, 2023.
The conference was held in a hybrid model to allow both in-person and online attendance.
Genetic Programming (GP) is a unique branch of evolutionary computation that has
been developed to automatically solve design problems, in particular computer program
design, without requiring the user to know or specify the form or structure of the solution
in advance. It uses the principles of Darwinian evolution to approach problems in the
synthesis, improvement, and repair of computer programs. The universality of computer
programs, and their importance in so many areas of our lives, means that the automation
of these tasks is an exceptionally ambitious challenge with far-reaching implications. It
has attracted a very large number of researchers and a vast amount of theoretical and
practical contributions are available by consulting the GP bibliography1.
Since the first EuroGP event in Paris in 1998, EuroGP has been the only conference
exclusively devoted to the evolutionary design of computer programs and other computational structures. In fact, EuroGP represents the single largest venue at which GP
results are published. It plays an important role in the success of the field, by serving as
a forum for expressing new ideas, meeting fellow researchers, and initiating collaborations. It attracts scholars from all over the world. In a friendly and welcoming atmosphere
authors present the latest advances in the field, also presenting GP-based solutions to
complex real-world problems.
EuroGP 2023 received 38 submissions from around the world. The papers underwent
a rigorous double-blind peer review process, each being reviewed by multiple members
of an international Program Committee.
Among the manuscripts presented in this volume, 14 were accepted for full-length
oral presentations (38% acceptance rate) and 8 as short talks. In 2023, papers submitted to
EuroGP could also be assigned to the "Evolutionary Machine Learning Track". Among
the 38 submissions, the authors of 9 papers indicated their papers fit the track, with
4 accepted for full-length oral presentation and one as short talks. Authors of both
categories of papers also had the opportunity to present their work in poster sessions to
promote the exchange of ideas in a carefree manner.
The wide range of topics in this volume reflects the current state of research in
the field. The collection of papers covers interesting topics including developing new
variants of GP algorithms for both optimization and machine learning problems as well
as exploring GP to address complex real-world problems