Research in the natural computing cluster covers theoretical foundations, the development of new algorithms, and interdisciplinary applications of natural computing methods.

The driving force behind their research is the mission to increase the understanding of natural systems as models of computation, with a focus on the development of new algorithms and applications to challenging problems. They investigate fundamental aspects of those algorithms as well as their applications to practical problems, including e.g. medicinal chemistry, pharmaceutical, physics, and engineering applications, as well as business applications ranging from portfolio optimization to forecasting. 
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Multicriteria Optimization and Decision Analysis

The focus of the Multicriteria Optimization and Decision Analysis (MODA) group is to develop foundations of methods in multi-objective optimization. Their interest lies in finding methods that simultaneously consider different performance criteria, which find solutions that are acceptable in practice, or provide insight into the trade-offs. For this, the group uses and designs algorithms that are implemented in modern computation environments. The group thus deals with theoretical foundations of the field such as algorithmic learning theory, optimization and order theory, and aspects related to algorithm engineering to bring results from theory into practice. 
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Explainable AI

The XAI (Explainable Artificial Intelligence) research group at Leiden University focuses on making AI and Evolutionary Computing (EC) systems more understandable and interpretable. Led by Dr. Niki van Stein, the team delves into methods and techniques that allow for the explanation of AI decision-making processes, aiming to enhance transparency and trust in AI technologies. Their work spans various scientific domains and industry applications, such as predictive maintenance, the analysis of heuristic optimization algorithms and the development of novel explainable AI methods. This interdisciplinary effort involves collaboration with experts in machine learning, optimization, and domain-specific experts to develop explainable systems that are both effective and user-friendly.
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