Presentation
About the workshop
Hybrid Intelligent Systems (HIS) deal with real-world complexity with a multidisciplinary approach and a plurality of artificial intelligence techniques. Complex systems, including biology, medicine, logistics, management, engineering, humanities, industrial environments, and technological applications, have significant difficulty modeling and interacting with their processes using classical methods. This workshop aims to discuss research on progress working with hybrid intelligent systems applied to topics applying artificial intelligence techniques in this framework.
The HIS2026 is a workshop conference held by the Mexican Society of Artificial Intelligence (SMIA) in its central Mexican International Conference on Artificial Intelligence (MICAI).
HIS2026 covers and gathers research topics associated with Hybrid Intelligent Systems and their capabilities for modeling, negotiating a specific topic, demonstrating reputation using diverse models, and managing all these complex processes.
Keynote Speaker XIX HIS 2026
Dr. Diego Alberto Oliva Navarro
Associate Professor of Computer Science, University of Guadalajara
(CUCEI).
BIOGRAPHY:
Dr. Diego Oliva is an Associate Professor of Computer Science at the University of Guadalajara (CUCEI), Mexico, where he has been a faculty member since 2017. He currently serves as a Visiting Professor at CICATA Tamaulipas of the National Polytechnic Institute (IPN), Mexico, and at North-West University (NWU), South Africa. From 2017 to 2018, he was a Visiting Professor at Tomsk Polytechnic University, Russia.
He received his Ph.D. in Informatics from the Complutense University of Madrid in 2015 and holds Level II distinction in Mexico’s National System of Researchers (SNII). He has supervised numerous master’s and doctoral students and has published extensively in indexed journals, books, book chapters, and international conference proceedings.
According to Google Scholar, Dr. Oliva has an h-index of 68 and more than 14,100 citations. He is included among the world’s top 2% most-cited scientists in the Stanford University study published by Elsevier and is ranked among the top five computer scientists in Mexico by Research.com.
Dr. Oliva is an IEEE Senior Member and a member of the Mexican Academy of Computing (AMEXCOMP). He currently serves as an Associate Editor for several leading international journals, including Swarm and Evolutionary Computation, Knowledge-Based Systems, The Journal of the Franklin Institute, IEEE Transactions on Artificial Intelligence, and IEEE Access, among others. He is also actively involved in major international conferences, including EvoStar, IEEE WCCI, IEEE CEC, GECCO, and IEEE SSCI.
His research interests include evolutionary computation, swarm intelligence, generative artificial intelligence for automated algorithm design, evolutionary explainability, explainable evolutionary artificial intelligence, hybrid and adaptive optimization methods, computational intelligence, computer vision, image processing, and metaheuristic optimization.
Title:
From Evolutionary Search to Evolutionary Intelligence: Generative AI for Automated Algorithm Design
ABSTRACT:
Evolutionary Computation has traditionally focused on searching for optimal solutions, while the design of optimization algorithms has remained largely dependent on human expertise. The emergence of Large Language Models (LLMs) and Generative AI is opening a new paradigm in which artificial intelligence can actively participate in algorithm configuration, operator generation, and automated algorithm design.
This keynote presents recent advances in LLM-driven evolutionary optimization, including automatic parameter configuration, AI-generated mutation operators, and performance-guided algorithm refinement. Building upon these developments, it discusses the transition from evolutionary search to evolutionary intelligence, where foundation models become active collaborators in designing, adapting, and improving optimization algorithms.
Finally, the talk outlines a vision for future Hybrid Intelligent Systems in which evolutionary algorithms, generative AI, and autonomous agents work together to accelerate optimization and scientific discovery.