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This thesis investigates two distinct but related optimization problems: bilevel and minmax problems, in the context of evolutionary algorithms (EAs). Bilevel optimization involves hierarchical decision-making, where decisions at the upper level are subject to constraints defined by the solutions of an optimization problem at the lower level. These problems are …

Rapid population growth and increasing water demand places a serious strain on both quantity and quality of available local water resources. Climate change puts additional pressure by altering the water balance, affecting recharge conditions, and contributing to pollution and water degradation. Addressing these issues requires water management strategies that enhance …

In this thesis, we introduce novel methods for equation discovery (ED), based on the use of probabilistic grammars. ED and symbolic regression address the task of finding a symbolic mathematical model that best describes observed data. Models can be as simple as an algebraic equation or as complex as a …

Despite the large volume of recently published papers in the field of constrained multiobjective optimization, the understanding and characterization of constrained multiobjective optimization problems (CMOPs) for benchmarking multiobjective evolutionary algorithms (MOEAs) and the related constraint handling techniques (CHTs) remain unsatisfactory. Therefore, selecting appropriate CMOPs for benchmarking is challenging and lacks …

In optimization, it is well known that algorithm performance is dependent on the problem being solved. As a consequence of this, achieving good optimization results requires correctly matching an optimization problem to a specific optimization algorithm that performs well on that problem. For this to be possible, knowledge of both …

The thesis addresses a novel representation learning framework, combining neural and symbolic text representations, and demonstrates its utility for tackling diverse natural language processing problems. The proposed approach, avoiding the deficiencies of purely symbolic and purely neural methods, can be applied for the generation of efficient text representations. Its usefulness …

Robots that are supposed to perform human-like tasks must possess appropriate skills to carry them out. In unstructured environments and for complex tasks, these skills are difficult to pre-program due to the complexity of the real world. It is therefore advantageous if robots have the ability to acquire the necessary …

In this thesis, we integrate complex nodes into predictive clustering trees (PCTs). PCTs are well-established machine learning models that are very flexible in terms of the machine learning tasks that they can address, including structured output prediction and semisupervised learning. Like standard decision trees, they are learned with a greedy …

This thesis covers a study of the photoexcited relaxation dynamics in superconducting ironbased pnictides, cuprates and charge-density wave systems by means of femtosecond laser spectroscopy. To examine the temperature and fluence dependence of photoinduced reflectivity transients ΔR/R in iron-based pnictides and cuprates, we apply a standard double pump-probe technique, while …

SRAM-based FPGAs have become an attractive solution for many applications where a short development time, low-cost for low-production volumes, and in-the-field-programming ability are important issues. The flexibility of SRAM-based FPGAs comes from the adoption of a configuration memory that defines the operations of the circuit that the FPGA implements. It …