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)

ARC prize: a call to arms for Genetic Programming by Alberto Tonda

The Abstraction and Reasoning Corpus (ARC) is a benchmark designed to be easy to solve for humans and next to impossible for machine learning techniques which rely upon massive training data sets, like Deep Learning. Google's François Chollet, the author,  presents ARC as an attempt to push for AI algorithms able to "learn like humans" [1], or in other words, able to solve tasks after seeing just a small number of training instances, exploiting innate capacities to reason on geometry and number [2]. Just a few weeks ago, Chollet announced a Kaggle challenge on ARC, with a prize of 1 million $ [3] and a first deadline in November 2024, although submissions are already open [4].

I am not affiliated with the prize in any way, but I think this could represent a valuable opportunity for the GP/EA community: the tasks in ARC can be solved through *program synthesis*, as stated by Chollet himself [5], a function GP excels at; and the capacity of learning from a few samples is another strong suite of GP. Not to mention, just participating in the contest and comparing against other approaches could lead to cross-fertilization of new ideas, and even help promote GP as a robust AI alternative to the now more prominent DL approaches.

After discussing with colleagues, I decided to spread the news far and wide, in the hope that more and more people from our community would decide to take up the challenge. The current state of the art performance on ARC is still low (less than 40% accuracy on test at the time of writing), so the entry barriers should not be that high: now it's a good moment for GP researchers to take on the world and test our mettle!

[1] https://arxiv.org/pdf/1911.01547 

[2] https://aiguide.substack.com/p/why-the-abstraction-and-reasoning

[3] https://arcprize.org/competition

[4] https://www.kaggle.com/competitions/arc-prize-2024/

[5] https://arcprize.org/guide

Applications of Genetic Programming

An oft posed question is how much is genetic programming used, "for real"? https://gpbib.cs.ucl.ac.uk/gp-html/jaws30_reply.html Today, although many papers propose new types of GP, most are about applying GP.  Many papers use real world datasets to show how good a novel form of GP is or to compare GP and other AI approaches.   Instead lets concentrate upon papers where GP is just being used and the application itself is the important thing.

Of course most industrialists are not interested in papers.  Indeed they may have sound commercial reasons for not publicising their results or even what they are interested in. Which always means numbers based on published work will be an underestimate.

Nonetheless, taking data for 2023 in the genetic programming bibliography https://gpbib.cs.ucl.ac.uk/ today as typical, about 38% (pm 5%) of papers are on applications.  About a quarter of all GP papers are on: Medicine, Civil Engineering or Material Science, often with an environmental or sustainability emphasis.

Humies 2024, closes Friday 31 May

A quick reminder GP+EM articles published after 2 June 2023 are eligible to enter this year's Human Competitive awards (competition closes to new entries on Friday May 31). Full details on line: https://human-competitive.org/call-for-entries

As with last year, the Humies will be held in hybrid mode, i.e. both in person in Melbourne and online, via video link. Prize money will be sent to the winners via wire transfer.

Entries listed in https://human-competitive.org/awards

The finalists who will battle it out to convince the judges they should be the winner have been announced on  https://human-competitive.org/awards

 

Continuous publication

Beginning with Volume 24, Genetic Programming and Evolvable Machines has adopted a "continuous publication" model, in which articles are published in open issues as soon as their production processes are complete. 

In this context it no longer seems to make sense to post new issue announcements on this blog. 

To see the latest published articles, please check the Volumes and Issues section of the journal's website.

Boost your paper's readership: GECCO 2023 Hot of the Press

The GECCO conference offers authors of recent GP+EM papers an opportunity to present it in front of an audience. See https://gecco-2023.sigevo.org/Call-for-HOPs

GPEM 23(4) is now available

The fourth issue of Volume 23 of Genetic Programming and Evolvable Machines is now available for download.

This issue includes papers in the Special Issue on Evolutionary Computation in Art, Music and Design.

It contains:

A novel tree-based representation for evolving analog circuits and its application to memristor-based pulse generation circuit
by Xinming Shi, Leandro L. Minku, and Xin Yao

Using estimation of distribution algorithm for procedural content generation in video games
by Arash Moradi Karkaj and Shahriar Lotfi

Complexity and aesthetics in generative and evolutionary art
by Jon McCormack and Camilo Cruz Gambardella

Experiments in evolutionary image enhancement with ELAINE
by João Correia, Daniel Lopes, Leonardo Vieira, Nereida Rodriguez-Fernandez, Adrian Carballal, Juan Romero and Penousal Machado

BOOK REVIEW
Melanie Mitchell: Artificial intelligence—a guide for thinking humans
by Didem Özkiziltan

BOOK REVIEW
Machado, Romero and Greenfield (editors): Artificial intelligence and the arts
by Anna Jordanous

BOOK REVIEW
The evolution of complexity
by Emily Dolson

BOOK REVIEW
Ying Bi, Bing Xue, Mengjie Zhang: Genetic programming for image classification—an automated approach to feature learning
by Amelia Zafra