New Collections OPEN for Submission

 It is my pleasure to announce that 2 NEW Collections are open for submissions in Genetic Programming and Evolvable Machines, under the editorship of Ting Hu (Queen's University, Canada), our Special Communications Editor at the journal. 

These Collections will be published as Special Sections in the journal, namely

- Section: Comments and Correspondence: https://link.springer.com/collections/hhbhihfefd

- Section: Perspectives and Vision: https://link.springer.com/collections/ejjafdcebh

In both cases, the link gives a brief description of the aims and scope of each Collection, but if you have any further questions please contact me (leonardo.trujillo@tectijuana.edu.mx) or Ting Hu directly (ting.hu@queensu.ca).

SUBMISSION INSTRUCTIONS:

In both cases, if you intended to submit you must perform these steps in the Submission system:
1 Article Type: Authors should select “Correspondence” (for both collections)

2 Select the appropriate Collection for the submission (either Comments and Correspondence OR Perspectives and Vision)

Hope to continue to receive your excellent contributions to the journal!

Leonardo Trujillo

Editor in Chief

Genetic Programming and Evolvable Machines

Call for Papers: Special Issue on Generative AI and Evolutionary Computation for Software Engineering

 Special Issue Home: https://link.springer.com/collections/bcadcgjdjd

Generative models, and mainly large language models, are already widely used tools in real-world software development. They assist with writing code, generating tests, fixing bugs, and more. While these tools are powerful and quite useful, they still struggle with reliability, maintainability, and meeting complex requirements. This is where evolutionary algorithms, and especially genetic programming techniques, can make a decisive contribution. Unlike generative models that primarily rely on learned patterns, evolutionary algorithms offer a search-based approach to systematically explore the solution spaces. By combining the strengths of generative models with the flexibility and robustness of search-based techniques, we could build hybrid systems that produce even better and more reliable results.

The focus of this special issue is on the integration of generative methods and evolutionary computation to advance software engineering tasks. It aims to highlight approaches where evolutionary methods enhance, guide, or refine the output of generative models to produce better software solutions.

Topics of interest include, but are not limited to:

• Program Synthesis
• Requirements Engineering
• Prompt Engineering / Guided Prompt Search
• Genetic Improvement (functional and non-functional improvement)
• Code Transplantation
• Code Translation
• Automated Refactoring
• Clone Detection and Elimination
• Automated Program Repair
• Test Generation
• Test Suite Improvement
• Code Explanations & Interpretability
• Documentation Generation
• Semantic Code Search
• Human-AI Collaboration Tasks

Key Dates:

• Submission Deadline: 1 December 2025
• Reviews: 1 March 2026
• Revision Deadline: 15 April 2026
• Final Acceptance Notification: 15 May 2026

Links

• Special Issue Collection : https://link.springer.com/collections/bcadcgjdjd
• Journal Home: https://link.springer.com/journal/10710
• Submission Guidelines: https://link.springer.com/journal/10710/submission-guidelines

Guest Editor:

·        Dominik Sobania, Johannes Gutenberg University, Mainz, Germany (dsobania@uni-mainz.de)

Thematic Area

·        Software Engineering (Editor, Justyna Petke, University College London, UK)