Workshops

The program and registration information for each workshop are available in the specific details for each one.

• HERIT-AI (Iberoamerican Workshop on Trustworthy Artificial Intelligence and Cultural Heritage)

• EMORA (International Workshop on Evolutionary Multiobjective Optimization and Real-World Applications)

• Knowledge-Driven Design of Metaheuristics and Hyper-Heuristics for Optimization Problems (KDDMH 2026)

All the workshops will be on Tuesday Nov, 17. Facultad Politécnica, campus of the Universidad Nacional de Asunción (UNA), View it on Google Maps.

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Summary of the workshops

Iberoamerican Workshop on Trustworthy Artificial Intelligence and Cultural Heritage (HERIT-AI)

Recent advances in Artificial Intelligence (AI) are creating new opportunities for documenting, preserving, analyzing, interpreting, managing, and disseminating cultural heritage. However, the use of AI in Cultural Heritage also raises significant risks and challenges. AI systems may reflect and reinforce biases, cultural stereotypes, or imbalances present in the data used to train them. In light of these challenges, HERIT-AI aims to bring together AI researchers and professionals from museums, archives, libraries, archaeology, conservation, digital humanities, and other Cultural Heritage institutions to foster interdisciplinary collaboration and knowledge exchange. Beyond original research papers, the workshop will provide a forum for discussing real-world experiences, case studies, pilot projects, and ongoing initiatives involving the adoption of AI in Cultural Heritage.

The vision of HERIT-AI is to build an Ibero-American community where Artificial Intelligence and Cultural Heritage evolve together. By encouraging dialogue, co-creation, and interdisciplinary collaboration, the workshop seeks not only to advance AI applications for cultural heritage, but also to inspire the development of a more human-centered, trustworthy, and socially meaningful Artificial Intelligence through one of humanity’s most valuable domains.

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International Workshop on Evolutionary Multiobjective Optimization and Real-World Applications (EMORA)

EMORA is proposed as a satellite workshop of IBERAMIA dedicated to bringing together researchers, practitioners, and graduate students working on Evolutionary Multiobjective Optimization (EMO) and its application to challenging real-world problems. The workshop will provide a forum for presenting novel algorithms, hybrid optimization methods, benchmarking studies, and practical applications in engineering, artificial intelligence, healthcare, logistics, telecommunications, energy, manufacturing, agriculture, and other domains.

EMORA aims to strengthen the Ibero-American research community by encouraging interdisciplinary collaboration and promoting the transfer of optimization techniques to real-world applications

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Knowledge-Driven Design of Metaheuristics and Hyper-Heuristics for Optimization Problems (KDDMH 2026)

Metaheuristics and hyper-heuristics are widely used to address challenging numerical or combinatorial optimization problems. Their development, however, often relies on iterative experimentation in which researchers make design decisions about representations, algorithmic components, parameters, search strategies, and adaptation mechanisms. While the performance of the resulting algorithms is usually the primary object of analysis, the knowledge generated throughout this design process is rarely treated as a first-class research outcome.

The proposed workshop aims to bring together researchers interested not only in designing effective metaheuristics and hyper-heuristics, but also in understanding, extracting, representing, learning, and reusing knowledge generated throughout the algorithm design process. In this context, algorithm design is viewed as a data- and knowledge-generating process in which experimental observations, problem-instance characteristics, algorithm components, parameter configurations, and performance outcomes can be transformed into reusable design knowledge.

The workshop seeks to promote a shift from a predominantly performance-oriented perspective toward a knowledge-driven perspective on optimization algorithm design. Particular emphasis will be placed on approaches that make design decisions more systematic, data-driven, explainable, transferable, and reproducible.