Workshop on Surrogate-Assisted Evolutionary Optimisation
Webpage: https://saeopt.bitbucket.io/
Description
In many real-world optimisation problems, evaluating the objective function(s) is expensive, perhaps requiring days of computation for a single evaluation. Surrogate-assisted optimisation attempts to alleviate this problem by employing computationally cheap 'surrogate' models to estimate the objective function(s) or the ranking relationships of the candidate solutions.
Surrogate-assisted approaches have been widely used across the field of evolutionary optimisation, including continuous and discrete variable problems, although little work has been done on combinatorial problems. Surrogates have been employed in solving a variety of optimization problems, such as multi-objective optimisation, dynamic optimisation, and robust optimisation. Surrogate-assisted methods have also found successful applications in aerodynamic design optimisation, structural design optimisation, data-driven optimisation, chip design, drug design, robotics, and many more. Most interestingly, the need for on-line learning of the surrogates has led to a fruitful crossover between the machine learning and evolutionary optimisation communities, where advanced learning techniques such as ensemble learning, active learning, semi-supervised learning and transfer learning have been employed in surrogate construction.
Despite recent successes in using surrogate-assisted evolutionary optimisation, there remain many challenges. This workshop aims to promote the research on surrogate assisted evolutionary optimization including the synergies between evolutionary optimisation and learning. Thus, this workshop will be of interest to a wide range of GECCO participants. Particular topics of interest include (but are not limited to):
- Bayesian optimisation
- Advanced machine learning techniques for constructing surrogates
- Model management in surrogate-assisted optimisation
- Multi-level, multi-fidelity surrogates
- Complexity and efficiency of surrogate-assisted methods
- Small and big data-driven evolutionary optimization
- Model approximation in dynamic, robust, and multi-modal optimisation
- Model approximation in multi- and many-objective optimisation
- Surrogate-assisted evolutionary optimisation of high-dimensional problems
- Comparison of different modelling methods in surrogate construction
- Surrogate-assisted identification of the feasible region
- Comparison of evolutionary and non-evolutionary approaches with surrogate models
- Test problems for surrogate-assisted evolutionary optimisation
- Performance improvement techniques in surrogate-assisted evolutionary computation
- Performance assessment of surrogate-assisted evolutionary algorithms
Submission format
Full papers and extended abstracts:
- Full papers (8 pages + references): Must cover the ACM Open APC (see below for more information)
- Extended Abstracts (up to 4 pages): Are not eligible for APC - no fee paid by the authors for ACM Open Access. An Extended Abstract provides a summary of a work-in-progress, typically just enough for readers to understand the idea, scope, and potential impact. It often lacks full methodology, detailed results, or extensive references.
Important dates
- Submission opening: February 2, 2026
- Submission deadline:
March 27, 2026April 03, 2026 - Notification: April 24, 2026
- Camera-ready: May 5, 2026
- Author's mandatory registration: May 11, 2026
ACMs new Open Access publishing model for 2026 ACM Conferences
Starting January 1, 2026, ACM will fully transition to Open Access. All ACM publications, including those from ACM-sponsored conferences, will be 100% Open Access. Authors will have two primary options for publishing Open Access articles with ACM: the ACM Open institutional model or by paying Article Processing Charges (APCs). With over 2,600 institutions already part of ACM Open, the majority of ACM-sponsored conference papers will not require APCs from authors or conferences (currently, around 76%).
Authors from institutions not participating in ACM Open will need to pay an APC to publish their papers, unless they qualify for a financial waiver. To find out whether an APC applies to your article, please consult the list of participating institutions in ACM Open and review the APC Waivers and Discounts Policy. Keep in mind that waivers are rare and are granted based on specific criteria set by ACM.
Understanding that this change could present financial challenges, ACM has approved a temporary subsidy for 2026 to ease the transition and allow more time for institutions to join ACM Open. The subsidy will offer:
- $250 APC for ACM/SIG members
- $350 for non-members
This represents a 65% discount, funded directly by ACM. Authors are encouraged to help advocate for their institutions to join ACM Open during this transition period.
This temporary subsidized pricing will apply to all conferences scheduled for 2026.
Additionally, SIGEVO will provide an additional subsidy of $125 to papers accepted to GECCO 2026 (and only for 2026) that are subject to APCs. This will make the final amounts to be paid:
- $125 (USD) for SIGEVO members
- $225 (USD) for non-members
It is IMPORTANT to mention that both forms of subsidy (by ACM and by SIGVO) only apply to GECCO 2026. Moreover, it is still to be determined how the SIGEVO subsidy will be implemented, either directly to the APC or in other forms.
Finally, we note that APC charges apply to accepted Full Papers, but Abstracts (1-2 pages), Extended Abstracts (1-4 pages) and Tutorials ARE NOT APC Eligible; i.e., an APC will not have to be paid for these types of contributions.
ACM Authorship and Peer Review Policies on Generative AI
GECCO follows the official ACM policies on authorship and peer review, including the use of generative AI tools.
Under ACM's Authorship policy, generative AI tools and technologies cannot be listed as authors of an ACM published Work. The use of generative AI tools and technologies for assistance must be fully disclosed in the manuscript's Acknowledgments section. Authors are fully accountable for the originality, accuracy, and integrity of all submitted material.
In accordance with ACM's Peer Review policy, reviewers must not upload or share submitted manuscripts or review materials with generative AI systems. Reviewers may use generative AI or tools with the sole purpose of improving the quality and readability of reviewer reports for the author.
ACM is actively developing tools to help identify improper AI use in submissions, and GECCO may employ available detection methods. Submissions found to violate ACM policies may be rejected.
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Organizers
Dr Rahat is an Associate Professor of Data Science. His expertise is in evolutionary and Bayesian search and optimisation. Particularly, he has worked on developing effective acquisition functions for optimising single and multi-objective problems and locating the feasible space of solutions. He has a strong track record of working with industry on a broad range of optimisation problems, which resulted in numerous articles in top journals and conferences, including a best paper in the Real-World Applications track at GECCO, and a patent with Hydro International Ltd. Recently, he has been actively contributing to the Welsh Government's response to the pandemic using his expertise in machine learning and parameter optimisation with funding from both the Welsh Government (Co-PI and Co-I; £750k) and EPSRC (EP/W01226X/1, PI; £230k). His work, with colleagues at Swansea, has resulted in generating medium-term projections of admissions and deaths every week for the First Minister of Wales, and the UK Health Security Agency.
He is one of 24 members of the IEEE Computational Intelligence Society Task Force on Data-Driven Evolutionary Optimization of Expensive Problems. He has been the lead organiser for the Surrogate-Assisted Evolutionary Optimisation (SAEOpt) workshop at GECCO since 2016, and was the Proceedings Chair for GECCO 2022. Furthermore, he successfully led Swansea University's application to join the Turing University Network in 2023, and he is currently the Turing Academic Liaison for the university.
Currently, he is interested in developing methods for optimising constrained and expensive single and multi-objective problems, and active learning, that may be applied in different contexts, e.g. engineering design, educational technology, computational modelling, decision-making, and policy exploration.
Dr Rahat has a BEng (Hons.) in Electronic Engineering from the University of Southampton, UK, and a PhD in Computer Science from the University of Exeter, UK. He completed a Postgraduate Certificate in Teaching in Higher Education at Swansea University, and he is now a fellow of the Higher Education Academy (FHEA). He worked as a product development engineer after his bachelor's degree, and held post-doctoral research positions at the University of Exeter. Before moving to Swansea, he was a Lecturer in Computer Science at the University of Plymouth, UK.