Multimodal Data-Driven Optimization and Learning
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Description
The Multimodal Data-Driven Optimization and Learning (MultiDOL) workshop focuses on addressing the growing need for integrating diverse data modalities—such as images, text, and sensor data—into evolutionary learning and optimization frameworks. As real-world problems increasingly involve heterogeneous data sources, from autonomous systems combining visual and LiDAR data to healthcare applications fusing medical images and clinical records, traditional learning and optimization approaches must evolve.
This half-day workshop explores novel methods for multimodal data integration in evolutionary algorithms, including multimodal representation learning, cross-modal feature fusion, and hybrid EC-deep learning approaches. We welcome contributions on theoretical foundations, algorithm design, benchmark development, and applications across areas, such as robotics, smart manufacturing, healthcare, and environmental monitoring.
Through three invited talks from leading experts and peer-reviewed paper presentations, the workshop brings together researchers from evolutionary computation, machine learning, computer vision, and application domains to address key questions: How can evolutionary algorithms effectively process information from multiple data modalities? What new optimization paradigms emerge when combining EC with modern multimodal AI including foundation models? How do we design appropriate benchmarks for these problems?
MultiDOL aims to foster collaborations between the EC community and multimodal AI researchers, establishing a foundation for sustained research in this emerging interdisciplinary area.
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
Bing Xue is currently Professor of Artificial Intelligence, and Deputy Head of School in the School of Engineering and Computer Science at Victoria University of Wellington. Her research focuses mainly on evolutionary computation, machine learning, big data, feature selection/learning, evolving neural networks, explainable AI and their real-world applications. Bing has over 400 papers published in fully refereed international journals and conferences including many highly cited papers and top most popular papers. Bing was the Editor of IEEE CIS Newsletter, Chair of the Evolutionary Computation Technical Committee, member of ACM SIGEVO Executive Committee and Chair of IEEE CIS Task Force on Evolutionary Deep Learning and Applications. She also chaired the IEEE CIS Data Mining and Big Data Technical Committee, Students Activities committee, and a member of many other committees. She founded and chaired IEEE CIS Task Force on Evolutionary Feature Selection and Construction, and co-founded and chaired IEEE CIS Task Force on Evolutionary Transfer Learning and Transfer Optimisation. She also won a number of awards including Best Paper Awards from international conferences, and Early Career Award, Research Excellence Award and Supervisor Award from her University, IEEE CIS Outstanding Early Career Award, IEEE TEVC Outstanding Associate Editor and others.
Bing has also been served as an Associate/Guest Editor or Editorial Board Member for > 10 international journals, including IEEE TEVC, ACM TELO, IEEE TETCI, IEEE TAI, and IEEE CIM. She is a key organiser for many international conferences, e.g. General Chair of PRICAI 2025 and IVCNZ 2025, Conference Chair of EuroGP 2025 and 2024, Conference Chair of IEEE CEC 2024, ambassador for Women in Data Science NZ 2025, 2024, and 2023, Chair Women+@GECCO 2024, Proceeding Chair of GECCO 2023, Tutorial Chair for IEEE WCCI 2022, Publication Chair of EuroGP 2022, Track Chair for ACM GECCO 2019-2022, Workshop Chair for IEEE ICDM 2021, General Co-Chair of IVCNZ 2020, Program Co-Chair for KETO 2020, Senior PC of IJCAI 2019-2021, Finance Chair of IEEE CEC 2019, Program Chair of AJCAI 2018, IEEE CIS FASLIP Symposium founder and Chair since 2016, and others in international conferences. More can be seen from her website.