Welcome to Volume 16 of Genetic Programming and Evolvable Machines.

Over the past year the journal has continued to thrive, attracting a steady and increasing stream of strong submissions and publishing some of the most innovative and high-quality work within the journal’s scope. Our editorial board has also been doing an exemplary job, decreasing the average number of days from submission to first decision while providing thoughtful guidance to authors, both for the papers that we accept and for those that we reject.

Volume 15 was particularly “special” in that it included a special section on “Evolvability and Robustness in Artificial Evolving Systems,” a special peer commentary section on “Genetic Programming and Emergence” (focused on a target article by the journal’s founding editor Wolfgang Banzhaf), and an entire special issue on “GECCO Competitions.” All of these special features were novel contributions to the literature, and I hope and expect that they will set trends that will be followed both in this journal and in others.

Volume 16, by contrast, will be relatively “regular” insofar as we have a full queue of regular submissions that we plan to publish over the first three issues. At the time of this writing the deadline for submissions to a special issue on “Semantic Methods in Genetic Programming,” guest edited by Michael ONeill, is approaching; if this process proceeds as expected then the special issue will be published as the fourth issue of Volume 16.

The present issue includes two articles that aim to improve genetic programming’s capacity for producing results that generalize to unseen data: “Training genetic programming classifiers by vicinal-risk minimization,” by Ji Ni and Peter Rockett, and “Improving GP generalization: a variance-based layered learning approach,” by Maryam Amir Haeri, Mohammad Mehdi Ebadzadeh, and Gianluigi Folino. It also includes an article on a novel approach, involving genetic algorithms, for solving problems in quantum information theory: “GA-based approach to find the stabilizers of a given sub-space,” by Mahboobeh Houshmand, Morteza Saheb Zamani, Mehdi Sedighi, and Monireh Houshmand. Finally, the issue includes two pieces on open source software for genetic programming: “A C++ framework for geometric semantic genetic programming,” by Mauro Castelli, Sara Silva, and Leonardo Vanneschi, and “Introducing a cross platform open source Cartesian Genetic Programming library,” by Andrew James Turner and Julian Francis Miller.

I hope that you will find these contributions and the work that will be published in the remainder of Volume 16 to be both interesting and informative. I also hope that those of you who are conducting new research will consider submitting your manuscripts to Genetic Programming and Evolvable Machines, and that you will feel free to contact me with questions, comments, and ideas.