Decomposition Techniques in Evolutionary Optimization
Webpage: https://sites.google.com/view/dteo/
Description
Decomposition-based optimization involves transforming a complex problem into multiple smaller, more manageable sub-problems that can be solved cooperatively. The evolutionary computing community actively develops methods to explicitly or implicitly design decomposition across four key facets: (i) environmental parameters, (ii) decision variables, (iii) objective functions, and (iv) available computing resources.
This workshop aims to bring together recent advances in the design, analysis, and understanding of evolutionary decomposition techniques. It also provides a platform to discuss challenges in applying decomposition to increasingly large and complex optimization tasks—such as problems with many variables or objectives, multi-modal problems, simulation-based optimization, and uncertain scenarios—while considering modern large-scale computing environments, including massively parallel and decentralized systems.
The workshop focuses on, but is not limited to, the following topics:
- Large-scale evolutionary decomposition: Decomposition in decision space, gray-box methods, --co-evolutionary algorithms, grouping techniques, and cooperative methods for constraint handling.
- Many- and multi-objective decomposition: Aggregation and scalarization methods, hybrid island-based approaches, and (sub-)population decomposition and mapping.
- Parallel and distributed evolutionary decomposition: Scalability across decision and objective spaces, decentralized divide-and-conquer strategies, distributed computing efforts, and deployment on heterogeneous, large-scale parallel platforms.
- New general-purpose decomposition techniques: Machine-learning-assisted decomposition, online and offline configuration, search region decomposition, use of multiple surrogates, and parallel approaches for expensive optimization.
- Emerging applications of evolutionary techniques based on decomposition.
- Understanding and benchmarking decomposition techniques.
- Software tools and libraries for evolutionary decomposition.
In general, this workshop encourages both theoretical and practical contributions, focusing on developmental, implementation, and applied aspects of decomposition techniques in evolutionary optimization.
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