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Latin American applications of Optimization and AI

Webpage: https://sites.google.com/view/workshopgecco2026/home

Description

The Optimization and Artificial Intelligence in Latin America workshop brings together researchers and practitioners interested in the design, analysis, and application of optimization and machine learning with a special focus on the region’s challenges, data, and use cases. While both fields search for solutions and models, their internal dynamics—structural biases, behavioral transitions, exploration–exploitation, robustness, any-time performance, and complexity—remain insufficiently understood due to the nature of heuristic processes and experimental practices that often emphasize final outcomes only. We therefore welcome theoretical and empirical contributions that instrument executions—online or offline—to model population dynamics, identify desirable algorithmic traits, detect failure modes, and improve interpretability, focused in problems of interest to the latin american region.

- The workshop focuses on, but is not limited to, the following topics:

    • Emerging applications in LATAM: Case studies in public health and hospitals, energy and smart grids, agribusiness, logistics and transportation, inclusive finance, smart cities, and environmental management; approaches robust to scarce/noisy data and domain shift.

    • Understanding, traceability, and benchmarking of AI–optimization pipelines: Analysis of internal dynamics and structural biases; exploration–exploitation balance and any-time measures; reproducibility (artifacts, scripts, baselines); benchmark suites with regional data; and transparent evaluation practices.

    • Safe AI, calibration, and uncertainty estimation (UQ): Methods to estimate, calibrate, and communicate model uncertainty, detect failures and out-of-distribution data, and quantify risk for safer decision-making.


Synergy with BIP Conference 2025 and the local community: This workshop is strategically aligned with the 7th IEEE International Conference on BioInspired Processing (BIP 2025), to be held in Costa Rica (UNA, Pérez Zeledón) on 3–5 December 2025. The BIP audience—bioinspired processing, nature-inspired computation, and AI applications—naturally converges with GECCO’s pillars (evolutionary computation, AI-driven optimization, evolutionary machine learning). We will leverage BIP to (i) announce this workshop’s CFP, (ii) invite authors with preliminary results to submit extended abstracts or full papers, and (iii) promote invited talks and a joint panel. The temporal continuity with GECCO 2026 in San José (13–17 July 2026) supports maturation and deeper discussion of contributions and broadens participation through the local community. The next iteration of the BIP conference, IEEE BIP 2026 will also be held in Costa Rica in november 2026. Additionally, the organizing committee will include co-chairs with prior workshop experience and active BIP committee members, ensuring the rigor of the review process.

The program will combine technical presentations, an interactive session (PANEL - Challenges and opportunities in Latin America for research and development), and invited talks when possible. We value reproducibility (artifacts, scripts, baselines), transparent reporting of configurations, and an honest discussion of limitations. Our goal is to map the state of the art at the AI–optimization intersection from and for Latin America, build bridges between regional groups and the international GECCO community, and foster sustained collaborations that accelerate scientific progress and its social and economic impact across the region.

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, 2026 April 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

Felipe Meza Obando

Dr. Felipe Meza-Obando has over 20 years of experience in the telecommunications industry and has combined his professional career with academic work as a Professor and Researcher at the Costa Rica Institute of Technology (TEC) and the University of Costa Rica (UCR). At TEC, he leads the ASTRIA research line within the Artificial Intelligence Laboratory for Natural Sciences, while at UCR he serves as a Scientific Researcher at the Laboratory of Solar Astrophysics and Space Weather, contributing to the improvement of predictions of solar activity affecting the interplanetary environment. He holds a Doctorate in Engineering, a Master of Science in Electronic Engineering with a specialization in Digital Signal Processing (DSP), and a Master Universitario in Astronomy and Astrophysics. His research focuses on the development and application of advanced artificial intelligence architectures to address complex problems in astrophysics, integrating DSP, machine learning, generative learning, and genetics algorithms.

Saul Calderón Ramírez

Dr. Saúl Calderón is a researcher, professor, and academic leader with experience in artificial intelligence, signal and image processing, machine learning, and high-performance computing. He holds a PhD in Computer Science from De Montfort University (United Kingdom) and a Master's degree in Electrical Engineering and a Bachelor's degree in Computer Science from the University of Costa Rica. He is currently a professor and researcher at the Costa Rica Institute of Technology (TEC), where he coordinates the PARMA (Pattern Recognition and Machine Learning) group. His career combines experience in applied research (Intel, CENAT, CITIC), scientific project leadership, technology development, and academic outreach. He has been a speaker at international forums such as Khipu 2023 and the AI ​​for Health Meeting (WHO/ITU), an organizer of multiple summer schools and symposia on machine learning, and a recipient of the Best Doctoral Thesis 2022-2023 award at his university.

Cindy Calderón-Arce

Cindy Calderón-Arce has a background in Computer-Assisted Mathematics Education, holds a Master’s degree in Applied Mathematics from the University of Puerto Rico, Mayagüez Campus, and a Ph.D. from DOCINADE with an emphasis in Applied Electronic Technologies. She is a Full Professor, researcher, and extensionist at the Costa Rica Institute of Technology (Instituto Tecnológico de Costa Rica). Her areas of interest include optimization, numerical analysis and surrogate modeling, as well as prediction, classification, and pattern recognition based on machine learning and deep learning.

Miguel Abreu Cárdenas

Miguel Guillermo Abreu Cárdenas is a Computer Engineer who graduated in 2020 from the Technological University of Havana, Cuba. He completed his Master’s degree in Computer Science at the Costa Rica Institute of Technology at the end of 2024 and is currently pursuing a Ph.D. in Engineering, where he researches uncertainty estimation in ophthalmic image segmentation using training techniques that require only small amounts of data. He has worked as a researcher at PARMA Group since 2023 and was previously a researcher providing teaching support at the Technological University of Havana (2021–2023). He has participated as a speaker at leading workshops on AI safety and other outreach activities.

Sebastian Rojas Gonzalez

Sebastian Rojas Gonzalez holds a degree in Industrial Systems Engineering (Costa Rica Institute of Technology), Master in Applied Mathematics (Harbin Institute of Technology, China), and PhD in Optimization (KU Leuven, Belgium). He is currently an FWO Postdoctoral Fellow at the Internet and Data Science Lab at Ghent University - IMEC, and a lecturer within the Computational Mathematics group at the Data Science Institute, both in Belgium. He is interested in multiobjective optimization, uncertainty quantification and model-based optimization.