Knowledge-Driven Design of Metaheuristics and Hyper-Heuristics for Optimization Problems (KDDMH 2026)
IBERAMIA 2026 Workshop
Date: Tuesday, November 17, 2026
Venue: Facultad Politécnica, Universidad Nacional de Asunción, San Lorenzo, Paraguay
Duration: 6 hours
Contact: octaramos@uv.mx
Call for Participation
The workshop Knowledge-Driven Design of Metaheuristics and Hyper-Heuristics for Optimization Problems aims to bring together researchers interested not only in designing effective metaheuristics and hyper-heuristics, but also in understanding, extracting, representing, learning, and reusing the knowledge generated throughout the algorithm design process.
The workshop promotes a shift from a predominantly performance-oriented perspective toward a knowledge-driven perspective on optimization algorithm design. Emphasis will be placed on approaches that make design decisions more systematic, data-driven, explainable, transferable, and reproducible.
We invite researchers, faculty members, postdoctoral researchers, PhD and MSc students, and practitioners working in artificial intelligence, computational intelligence, numerical and combinatorial optimization, metaheuristics, hyper-heuristics, machine learning, meta-learning, and automated algorithm design to participate in the workshop.
Topics of Interest
Topics of interest include, but are not limited to:
Metaheuristic and Hyper-Heuristic Design
- Metaheuristic and hyper-heuristic design
- Evolutionary algorithms
- Swarm intelligence
- Local search
- Memetic algorithms
- Hybrid optimization methods
- Algorithm components and search strategies
Knowledge Generation and Discovery
- Knowledge discovery from algorithm design experiments
- Meta-knowledge for algorithm design
- Design knowledge representation
- Explainable algorithm design
- Experimental algorithmics
- Algorithm engineering
- Analysis of algorithm behavior
Learning and Automation
- Algorithm configuration
- Algorithm portfolios
- Automated algorithm design
- Data-driven algorithm design
- Machine learning for algorithm design
- Learning-based hyper-heuristics
- Reinforcement learning
- Meta-learning
- Algorithm selection and performance prediction
Contributions
The workshop welcomes methodological and empirical contributions on knowledge-driven design of metaheuristics and hyper-heuristics. Contributions do not necessarily need to propose a new optimization algorithm; studies that extract, analyze, formalize, explain, validate, or reuse knowledge generated during the algorithm design process are particularly encouraged.
Submissions will consist of extended abstracts of up to 2 pages. Contributions may include:
- Research and empirical studies on knowledge-driven algorithm design and design decisions.
- Knowledge discovery studies extracting, analyzing, or formalizing knowledge from optimization experiments.
- Framework and methodology papers presenting data science pipelines, tools, or approaches for automated algorithm design.
- Position and perspective papers addressing new research directions in metaheuristic and hyper-heuristic design.
- Reproducibility and benchmark studies involving systematic comparisons, datasets, experimental protocols, or benchmark analyses.
Selected top-rated contributions may be invited to submit an extended version for fast-track review in a participating IBERAMIA journal, following the publication opportunities described in the official IBERAMIA 2026 Call for Papers (https://www.iberamia.org/iberamia/iberamia2026/wp-content/uploads/sites/19/2026/01/Call-for-Papers-IBERAMIA26.pdf).
Submission and Registration Information
Both theoretical and empirical contributions are welcome. Submission format, page limits, and final publication requirements will follow the official IBERAMIA 2026 workshop guidelines (https://link.springer.com/series/558/information-for-authors-and-editors).
Submission: Extended abstracts should be submitted electronically through the Microsoft CMT submission system using the following link: https://cmt3.research.microsoft.com/KDDMH2026
Paper Review Process
Submitted papers will be reviewed by at least two members of the Workshop Program Committee.
Each submission will be evaluated according to:
- Relevance to the workshop scope
- Originality and novelty
- Technical soundness
- Methodological rigor
- Experimental quality, when applicable
- Clarity of presentation
- Potential scientific impact
Both theoretical and empirical contributions are welcome.
Submission format, page limits, and final publication requirements will follow the official IBERAMIA 2026 workshop guidelines (https://link.springer.com/series/558/information-for-authors-and-editors).
Workshop Format
The workshop will last approximately 6 hours and will combine: Peer-reviewed paper presentations, Research discussions, and an interactive open discussion on challenges and future directions.
A central component of the workshop will be an open discussion focused on identifying research challenges and opportunities for transforming experimental evidence into reusable knowledge for the design of optimization algorithms.
The workshop is intended to be interactive and to encourage exchange among researchers working from different perspectives, including optimization, artificial intelligence, machine learning, meta-learning, and automated algorithm design.
Important Dates
The following dates are planned for the workshop submission and review process:
Table 1. Important dates for the workshop.
| Stage | Proposed Date |
| Call for Papers | September 15, 2026 |
| Paper Submission Deadline | October 15, 2026 |
| Notification of Acceptance | October 20, 2026 |
| Workshop | November 17, 2026 |
Organizing Committee
Marcela Quiroz-Castellanos, PhD
Universidad Veracruzana, Mexico
Experimental Algorithmics, Metaheuristics, Combinatorial Optimization, Computational Intelligence.
Octavio Ramos-Figueroa, PhD
Universidad Veracruzana, Mexico
Metaheuristics and Optimization, Automated Algorithm Design, Data-Driven Optimization, Algorithm Behavior Analysis, Hyper-Heuristic Design, Parameter Control Schemes.
Program Committee
- Genoveva Vargas-Solar, PhD — Centre National de la Recherche Scientifique, France
- Efrén Mezura-Montes, PhD — Universidad Veracruzana, Mexico
- Laura Cruz-Reyes, PhD — Instituto Tecnológico de Madero, Mexico
- Carlos Ignacio Hernández Castellanos, PhD — Universidad Nacional Autónoma de México, Mexico
- Rolando Menchaca-Méndez, PhD — Instituto Politécnico Nacional, Mexico
- Guadalupe Carmona Arroyo, PhD — Universidad Autónoma del Estado de Hidalgo, Mexico
- Aldo Márquez-Grajales, PhD — Universidad Autónoma del Estado de Hidalgo, Mexico
- Jesús Adolfo Mejía-de-Dios, PhD — Universidad Autónoma de Coahuila, Mexico
- Nancy Pérez-Castro, PhD — Universidad Veracruzana, Mexico
- Adriana-Laura López-Lobato, PhD — Universidad Veracruzana, Mexico
- Saúl Domínguez-Isidro, PhD — Universidad Veracruzana, Mexico
- Reynaldo Domínguez Castillo, PhD — Universidad Veracruzana, Mexico
- Santiago de Jesús González Medellín, MSc — Universidad Veracruzana, Mexico
The Microsoft CMT service was used for managing the peer-reviewing process for this conference. This service was provided for free by Microsoft and they bore all expenses, including costs for Azure cloud services as well as for software development and support.