Workshop on Evolutionary Computing and Explainable AI 2026

Description

The workshop will be held at the GECCO conference in San Jose, Costa Rica. GECCO runs 13-17 July. It will be both on-site and streamed online.

Programme

TBC

Call for papers

Explainable artificial intelligence (XAI) has gained significant traction in the machine learning community in recent years because of the need to generate “explanations” of how these typically black-box tools operate that are accessible to a wide range of users. From an application perspective, important questions arise, for which XAI may be crucial: Is the system biased? Has the problem been formulated correctly? Is the solution trustworthy and fair? The goal of XAI and related research is to develop methods to interrogate AI processes with the aim of answering these questions. This can support decision makers while also building trust in AI decision-support through more readily understandable explanations.

Nature-inspired optimisation techniques are also often black box in nature, and the attention of the explainability community has begun to consider explaining their operation too. Many of the processes that drive nature-inspired optimisers are stochastic and complex, presenting a barrier to understanding how solutions to a given optimisation problem have been generated. Explainable optimisation can address some of the above application-focused questions around bias, problem formulation, and trust, that also arise during the use of an optimiser.

By providing mechanisms that enable a decision maker to interrogate an optimiser and answer these questions trust is built with the system. On the other hand, many approaches to XAI in machine learning are based on search algorithms that interrogate or refine the model to be explained, and have the potential to draw on the expertise of the EC community. Furthermore, many of the broader questions (such as what kinds of explanation are most appealing or useful to end users) are faced by XAI researchers in general.

Following the success of the first four workshops hosted at GECCO 2022-25, we seek contributions on a range of topics related to this theme, including but not limited to:

Papers will be double blind reviewed by members of our technical programme committee.

Authors can submit short contributions including position papers of up to 4 pages and regular contributions of up to 8 pages following in each category the GECCO paper formatting guidelines. Software demonstrations will also be welcome.

Important dates

Submission

Workshop papers must be submitted using the GECCO submission system. After login, the authors need to select the “Workshop Paper” submission form. In the form, the authors must select the workshop they are submitting to. To see a sample of the “Workshop Paper” submission form, go to GECCO’s submission system and select “Sample Submission Forms”. Submitted papers must not exceed 8 pages (excluding references) and are required to be in compliance with the GECCO 2026 Papers Submission Instructions. It is recommended to use the same templates as the papers submitted to the main tracks. Each paper submitted to this workshop will be rigorously reviewed in a double-blind review process. In other words, authors should not know who the reviewers of their work are and reviewers should not know who the authors are. To this end, the following information is very important: Submitted papers should be ANONYMIZED. This means that they should NOT contain any element that may reveal the identity of their authors. This includes author names, affiliations, and acknowledgments. Moreover, any references to any of the author’s own work should be made as if the work belonged to someone else. All accepted papers will be presented at the ECXAI workshop and appear in the GECCO 2026 Conference Companion Proceedings. By submitting a paper, the author(s) agree that, if their paper is accepted, they will:

As a published ACM author, you and your co-authors are subject to all ACM Publications Policies, including ACM’s new Publications Policy on Research Involving Human Participants and Subjects.

Technical Programme Committee

TBC

Organisers (in alphabetical order)

Jaume Bacardit

jaume.bacardit@newcastle.ac.uk

Jaume Bacardit is Professor of Artificial Intelligence at Newcastle University in the UK. He has received a BEng, MEng in Computer Engineering and a PhD in Computer Science from Ramon Llull University, Spain in 1998, 2000 and 2004, respectively. Bacardit’s research interests include the development of machine learning methods for large-scale problems, the design of techniques to extract knowledge and improve the interpretability of machine learning algorithms, known currently as Explainable AI, and the application of these methods to a broad range of problems, mostly in biomedical domains. He leads/has led the data analytics efforts of several large interdisciplinary consortiums: D-BOARD (EU FP7, €6M, focusing on biomarker identification), APPROACH (EI-IMI €15M, focusing on disease phenotype identification) and PORTABOLOMICS (UK EPSRC £4.3M focusing on synthetic biology). Within GECCO he has organised several workshops (IWLCS 2007-2010, ECBDL’14), been co-chair of the EML track in 2009, 2013, 2014, 2020 and 2021, and Workshops co-chair in 2010 and 2011. He has 100+ peer-reviewed publications that have attracted 8500+ citations and a H-index of 41 (Google Scholar).

Alexander Brownlee

alexander.brownlee@stir.ac.uk

Alexander (Sandy) Brownlee is an Associate Professor in the Division of Computing Science and Mathematics at the University of Stirling, where he leads the Data Science and Intelligent Systems research group. His main topics of interest are in search-based optimisation methods and machine learning, with a focus on decision support tools, and applications in civil engineering, transportation and software engineering. He has published over 80 peer-reviewed papers on these topics. He has worked with several leading businesses including BT, KLM, and IES on industrial applications of optimisation and machine learning. He serves as a reviewer for several journals and conferences in evolutionary computation, civil engineering and transportation, was co-chair of the Real World Applications track at GECCO 2025 and 2026, and is currently an Editorial Board member for the journals Complex And Intelligent Systems and Journal of Scheduling. He has organised several workshops and tutorials at GECCO, CEC and PPSN on explainable AI and on genetic improvement of software.

Stefano Cagnoni

cagnoni@ce.unipr.it

Stefano Cagnoni graduated in Electronic Engineering at the University of Florence, Italy, where he also obtained a PhD in Biomedical Engineering and was a postdoc until 1997. In 1994 he was a visiting scientist at the Whitaker College Biomedical Imaging and Computation Laboratory at the Massachusetts Institute of Technology. Since 1997, he has been with the University of Parma, where he has been an Associate Professor since 2004. Awarded research grants include EU-funded grants on “Lifelong Prevention” and “High-level metrology services in food and nutrition for the enhancement of food quality and safety” within the “Next-generation EU” framework, an EU-funded “Marie Curie Initial Training Network” grant for a four-year research training project in “Medical Imaging using Bio-Inspired and Soft Computing”, and several collaborations with local firms. He has been Editor-in-chief of the “Journal of Artificial Evolution and Applications” from 2007 to 2010. From 1999 to 2018, he was chair of EvoIASP, an event dedicated to evolutionary computation for image analysis and signal processing within the Evostar multi-conference. From 2005 to 2020, he co-chaired MedGEC, a workshop on medical applications of evolutionary computation at GECCO. Co-editor of journal special issues dedicated to Evolutionary Computation for Image Analysis and Signal Processing and Evolutionary Computation for Explainable AI. Member of the Editorial Board of the journals “Evolutionary Computation” and “Genetic Programming and Evolvable Machines.” He received the “Evostar 2009 Award” for his outstanding contribution to Evolutionary Computation.

Martin Fyvie

m.fyvie@rgu.ac.uk

Martin Fyvie is a Research Fellow in Artificial Intelligence at Robert Gordon University, specialising in optimisation, explainable AI, and transparent decision-support systems for complex industrial and environmental applications. His PhD at RGU focused on developing novel methods for deriving post-hoc explanations from Genetic Algorithms and other evolutionary optimisation techniques, particularly interpreting black-box optimisation processes through high-volume algorithm trace data to analyse trade-offs and understand search behaviour. His research has expanded into applied AI, combining optimisation, data modelling, and socio-technical analysis across sectors including offshore decommissioning, net-zero skills planning, and rural mobility. As part of RGU’s Complex Optimization Group, he develops optimisation and modelling solutions for real-world problems enhanced through automation, simulation, and data-driven approaches. He has collaborated with industry partners to develop low-emission operational planning tools incorporating real-world constraints and contributed to national initiatives such as Data for Net Zero. Recent work includes serving as Co-Investigator on the Horizon Europe TH2ISTLE hydrogen valley project and the Smart Mobility Solutions for Rural Transport Optimisation project in the Orkney Islands as part of the Horizon RURALITIES project.

Giovanni Iacca

giovanni.iacca@unitn.it

Giovanni Iacca is an Associate Professor of Information Engineering at the Department of Information Engineering and Computer Science of the University of Trento, Italy, where he founded the Distributed Intelligence and Optimization Lab (DIOL). Previously, he worked as a postdoctoral researcher in Germany (RWTH Aachen, 2017-2018), Switzerland (University of Lausanne and EPFL, 2013-2016), and the Netherlands (INCAS3, 2012-2016), as well as in industry in the areas of software engineering and industrial automation. He is co-PI of the PATHFINDER-CHALLENGE project “SUSTAIN” (2022-2026). Previously, he was co-PI of the FET-Open project “PHOENIX” (2015-2019). He has received three best paper awards (D’IoT IEEE VTC-Spring 2025, EvoApps 2017, and UKCI 2012). His research focuses on computational intelligence, distributed systems, explainable AI, and analysis of biomedical data. In these fields, he co-authored more than 210 peer-reviewed publications. He is actively involved in organizing tracks and workshops at some of the top conferences on computational intelligence, and he regularly serves as a reviewer for several journals and conference committees. He is the General Chair of PPSN 2026. He is an Associate Editor for IEEE Transactions on Evolutionary Computation, Applied Soft Computing, Memetic Computing, Evolutionary Intelligence, Applied Intelligence, and Frontiers in Robotics and AI.

David Walker

D.J.Walker2@exeter.ac.uk

David Walker is a Senior Lecturer in Computer Science at the University of Exeter. He obtained a PhD in Computer Science in 2013 for work on visualising solution sets in many-objective optimisation. His research focuses on developing new approaches to solving hard optimisation problems with Evolutionary Algorithms (EAs), as well as identifying ways in which the use of Evolutionary Computation can be expanded within industry, and he has published journal papers in all of these areas. His recent work considers the visualisation of algorithm operation, providing a mechanism for visualising algorithm performance to simplify the selection of EA parameters. While working as a postdoctoral research associate his work involved the development of hyper-heuristics and, more recently, investigating the use of interactive EAs in the water industry. Dr Walker’s research group includes a number of PhD students working on optimisation and machine learning projects. He is active in the EC field, having run annual workshops on visualisation, interaction, and explainable AI within EC at GECCO since 2012 in addition to his work as a reviewer for journals such as IEEE Transactions on Evolutionary Computation, Applied Soft Computing, and the Journal of Hydroinformatics. He is a member of the IEEE Taskforce on Many-objective Optimisation.