We write as the outgoing and incoming Editors in Chief of Genetic Programming and Evolvable Machines (GPEM) to update readers regarding the status of the journal during this editorial transition.

The journal was launched in 1999 by Wolfgang Banzhaf, who served as Editor in Chief until 2009. Lee Spector served in this role until the time of this writing, in the summer of 2024. The transition to the next Editor in Chief, Leonardo Trujillo, is now in progress.

Over its history GPEM has aimed to provide a forum for research on the use of evolution-based methods to produce “active artifacts that interact with part of our environment,” including not only “software or algorithmic entities” but also “digital and analog electronic circuitry as well as robotics, and even active molecules or any other kind of machine that acts in our world” [1]. Because the journal’s community has largely been situated within the discipline of computer science, program-based approaches have been featured prominently, but the journal continues to foster a broader perspective on the evolution of active agents.

With the present transition in the chief editorship the goal is to sustain and continue to develop this broad vision of GPEM’s mission, building on the core elements of the journal that have already been established and aided by the ongoing efforts of the journal’s strong editorial board. In addition, a few incremental improvements and enhancements have been discussed and will be explored in the near future. For example, while the main publication category for GPEM will continue to be original research articles (augmented with resource reviews, so ably and tirelessly stewarded by Bill Langdon), we intend to enhance, promote and increase the number of submissions in the other categories such as letters (up to 6 pages), peer commentaries, and survey papers. As the trends in scientific publication continue to change, such as the growth of open access publication and the shift to a continuous publication model, GPEM will continue to work closely with Springer Nature to help ensure that these changes encourage and facilitate scientific advances and dialogue in our community.

This transition occurs at a pivotal moment for the broader ecosystem of AI and machine learning research, of which GPEM is a part. Academia, government and industry are all expressing both excitement and concern, identifying opportunities and pitfalls, in a field that is currently dominated by data-intensive statistical and neural methods [2]. Evolutionary methods may play an important role in the advancement, refinement, and control of these emerging technologies, much as synergistic interactions between learning and evolution have facilitated the development of adaptive and intelligent agents in nature.

One of the possible criticisms that can be made regarding the modern AI research program is that assigning the label of “intelligent” to any machine will continue to be a matter of choice, not fact, as long as the nature of biological intelligence continues to evade deeper understanding [3]. Nonetheless, there are certain properties of intelligence that are widely accepted, such as the ability of  “breaking out of any predetermined patterns” using “tangled recursion,” as Douglas R. Hofstadter pointed out when articulating the importance of developing “programs [machines] which can modify themselves – programs [machines] which can act on programs [machines], extending them, improving them, generalizing them, fixing them” (p. 152, [4]), issues which have been, and continue to be, at the core of GPEM’s focus.

We thank the many in the GPEM community—authors, editors, reviewers, and readers—who have so generously and effectively contributed their efforts and expertise to the journal and its mission in years past. We hope that you will all join us in meeting the challenges and exciting opportunities that we currently face.