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Seminario "Machine learning model selection using evolutionary algorithms and nature-inspired metaheuristics"

Dana Simian
Institución de origen
Lucian Blaga University of Sibiu, Romania



Machine learning methods have gained significant popularity in recent times for addressing problems across various fields. While employing machine learning libraries or pre-implemented methods is straightforward, it is critical to delve deeper into the challenges that affect these methods and employ specific techniques to overcome them. Equally important is the ability to correctly interpret results and make informed decisions when utilizing a machine learning model for problem-solving. The upcoming presentation will cover three main topics: 1. A brief overview of machine learning and its key challenges. 2. An introduction to evolutionary algorithms, including genetic and breeder algorithms, along with nature-inspired metaheuristics such as ant and wasp-based algorithms. 3. Addressing the machine learning model selection problem, exemplified by the optimal selection of Support Vector Machine (SVM) and Support Vector Regression (SVR) hyperparameters using evolutionary algorithms and a wasp optimization algorithm. The presentation is flexible, adjusting its focus to align with the audience's interests, knowledge levels, and familiarity with the topics discussed in this talk. 


Curriculum ponente  

Dana Simian holds a diploma in engineering from the University of Sibiu, Romania, and a diploma in Mathematics from the University Babes-Bolyai of Cluj-Napoca, Romania. She earned her Ph.D. from Babes-Bolyai University in Cluj-Napoca. Specializing in Informatics, she has completed numerous courses in the field. Currently, she serves as a professor in the Department of Mathematics and Informatics at the Faculty of Sciences, Lucian Blaga University of Sibiu, Romania. Additionally, she acts as the director of the Research Center in Informatics and Information Technology at the same university. Her areas of expertise encompass Machine Learning, Artificial Intelligence, Theory of Algorithms, Modeling, and Optimization. Her impactful contributions extend to a prolific publishing record, with books and articles featured in internationally recognized journals and presented at various international forums. Beyond her research accomplishments, she demonstrates a strong commitment to education with expertise in curriculum development within the field of Informatics and possesses extensive teaching experience.


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