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Feature construction, encompassing both feature engineering, which involves the manual design of features by domain experts, and representation learning, which refers to the automated discovery of useful data representations during model construction, is a fundamental aspect of machine learning. Its goal is to transform raw data into a more suitable …

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 …

The rapid advancements in Machine Learning (ML) and Black-Box Optimization (BBO) have led to an increased reliance on benchmarking data for evaluating and comparing algorithms across diverse domain tasks. However, the effective exploitation of this data is hindered by challenges such as syntactic variability, semantic ambiguity, and lack of standardization. …

Hydrogen is a promising energy carrier in the emerging green energy landscape. It is environmentally friendly, transportable, and storable. The Solid Oxide Cell (SOC) system is a complex technology that enables the bidirectional conversion of hydrogen to energy. The label SOC entails solid oxide fuel cell (SOFC) systems, which produce …

This thesis introduces a new optimization method based on deep learning, designed for data influenced by random processes. The main contribution of this method is the combination of advanced noise reduction techniques with recurrent neural network models, which helps to prevent the common problem of overfitting seen when there is …

Permanent magnets are vital parts of various devices, above all, those which are essential for the transition to a green society. However, they are made of materials, including rare-earth elements, for which unlimited availability cannot be taken for granted. Therefore, it is desirable to reduce the content of these materials …

The rapid evolution of sensor technologies, particularly within the Internet of Things (IoT) domain, has led to an era dominated by massive real-time data flows. This transition resulted in the need for novel data processing methodologies that facilitate the shift from traditional offline batch analytics to agile real-time predictions and …

In the field of biomedical materials, conventional approaches to tissue engineering and wound healing face limitations that encourage the exploration of innovative treatments. This work presents multifaceted exploration of piezoelectric polylactic acid (PLA) films intended for biomedical applications, from the optimization of the conditions for efficient film preparation to the …

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) is a versatile analytical method widely used in the field of surface science and thin films. Although it gives elemental, molecular, and isotopic information, it has a very low detection limit and high lateral resolution, is fast and works with all the vacuum-compatible samples, …

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 …