A common method to solve expensive function evaluation problem is using Bayesian Global Optimization, instead of Evolutionary Algorithms.

A modern ship design process is subject to a wide variety of constraints such as safety constraints, regulations, and physical constraints.

Promotores: Prof.dr. T.H.W. Bäck, Prof.dr. M.R.V. Chaudron, Co-Promotor: M.T.M. Emmerich

This thesis seeks to combine two different research topics; Multi-Objective Optimization and Complex Network Analysis.

Promotores: Prof.dr. T.H.W. Bäck, Prof.dr. M. Bongards (Cologne University)

Optimization tasks in practice have multifaceted challenges as they are often black box, subject to multiple equality and inequality constraints and expensive to evaluate.

It is a common technique in global optimization with expensive black-box functions to learn a surrogate-model of the response function from past evaluations and use it to decide on the location of future evaluations.

This thesis covers several aspects of quantum algorithms for near-term quantum computers and its applications to quantum chemistry and material science.

Multi-criteria optimalisatie is een effectieve techniek voor het vinden van optimale oplossingen die een afweging bieden tussen verschillende, tegenstrijdige criteria. Het heeft zijn toepassing gevonden in de wereld om ons heen omdat bij het oplossen van praktische, re¨ele wereld problemen men gewoonlijk…

This thesis involves three topics: benchmarking discrete optimization algorithms, empirical analyses of evolutionary computation, and automatic algorithm configuration.

In this thesis, topics relating to the optimization of high-throughput pipelines used for imaging are discussed. In particular, different levels of implementation, i.e., conceptual, software, and hardware, are discussed and the thesis outlines how advances on each level need to be made to make gains…

Continuous optimization is never easy: the exact solution is always a luxury demand and the theory of it is not always analytical and elegant.

Industrial manufacturing processes, such as the production of steel or the stamping of car body parts, are complex semi-batch processes with many process steps, machine parameters and quality indicators.

Multi-objective evolutionary computation aims to find high quality (Pareto optimal) solutions that represent the trade-off between multiple objectives.

Quantum annealing belongs to a family of quantum optimization algorithms designed to solve combinatorial optimization problems using programmable quantum hardware. In this thesis, various methods are developed and tested to understand how to formulate combinatorial optimization problems for quantum…

Promotor: Prof.dr. H.A.G. Wijshoff

Uncertainty and noise are frequently-encountered obstacles in real-world applications of numerical optimization. The practice of optimization that deals with uncertainties and noise is commonly referred to as robust optimization.

Carbon dioxide capture and utilization technologies are necessary to create a truly circular economy. The electrochemical reduction of carbon dioxide to formate is an appealing carbon utilization method as it can be performed at room temperature and pressure, it only requires two electrons, and it has…

Promotor: Prof.dr.ir. E. Deprettere

In design optimization problems, engineers typically handcraft design representations based on personal expertise, which leaves a fingerprint of the user experience in the optimization data. Thus, learning this notion of experience as transferrable design features has potential to improve the performance…

This thesis focuses on addressing four research problems in designing embedded streaming systems.

Promotor: Prof.dr. A. P. IJzerman, Co-Promotor: L.H. Heitman

Machine Learning is becoming a more and more substantial technology for industry.

Over several decades, a variety of computational methods for drug discovery have been proposed and applied in practice. With the accumulation of data and the development of machine learning methods, computational drug design methods have gradually shifted to a new paradigm, i.e. deep learning methods…

De Duitse hoogleraar Information Systems and Statistics Heike Trautmann aanvaardde deze maand de Pascalleerstoel bij LIACS, het informatica-instituut aan de Universiteit Leiden. Trautmanns belangrijkste vakgebieden zijn evolutionary multiobjective optimisation en data science, waar ook LIACS sterk in…

In this dissertation several settings in the Online Learning framework are studied. The first chapter serves as an introduction to the relevant settings in Online Learning and in the subsequent chapters new results and insights are given for both full-information and bandit information settings.

In this work we describe three methods to improve the performance of Quantum Field Theory calculations.

The next generation of high-contrast imaging instruments on space-based observatories requires sophisticated wavefront sensing and control in addition to a high-performance coronagraph.

Promotor: G.P. van Wezel, Co-Promotor: E. Vijgenboom

On a structural level, the properties featured by a majority of mechanical metamaterials can be ascribed to the finite number of soft internal degrees-of freedom allowing for low-energy deformations.

Promotors: Prof.dr. T.H.W. Bäck, Prof.dr. B. Sendhoff (Technische Universität Darmstadt)

Onderzoekers van het Leiden Institute of Advanced Computer Science (LIACS) ontwikkelen een systeem dat automatisch waarschuwingen uitstuurt wanneer onderdelen van motoren de eerste verschijnselen van vermoeidheid beginnen te vertonen. Het project duurt 4 jaar.

Inverse problems are problems where we want to estimate the values of certain parameters of a system given observations of the system. Such problems occur in several areas of science and engineering. Inverse problems are often ill-posed, which means that the observations of the system do not uniquely…

Quantum hardware comes with a different computing paradigm and new ways to tackle applications. Much effort has to be put into understanding how to leverage this technology to give real-world advantages in areas of interest for industries such as combinatorial optimization or machine learning.

This thesis introduces the concept of

In deep reinforcement learning, searching and learning techniques are two important components. They can be used independently and in combination to deal with different problems in AI, and have achieved impressive results in game playing and robotics. These results have inspired research into artificial…

Predictive maintenance (PdM) is a maintenance policy that uses the past, current, and prognosticated health condition of an asset to predict when timely maintenance should occur.

Promotores: T.H.W. Bäck, Y. Tan, Co-promotor: M.T.M. Emmerich

In this thesis, the discovery and optimization is described of chemical tools to study the N-acylethanolamine (NAE) biosynthetic pathway.

In this thesis it is posed that the central object of preference discovery is a co-creative process in which the Other can be represented by a machine. It explores efficient methods to enhance introverted intuition using extraverted intuition's communication lines.

Promotores: C.A.M.J.J. van den Hondel, V. Meyer, Co-promotor: A.F.J. Ram

Tuberculosis (TB) is associated with 1.5 million deaths annually. There is a need exists to optimize both current as well as novel antibiotic combination treatment strategies to improve the effectiveness and safety of treatments against TB.

Explaining treatment response variability between and within patients can support treatment and dosing optimization, to improve treatment of individual patients.

This thesis describes the design, synthesis, and immunological evaluation of varying (neo)antigenic peptide conjugates containing either a TLR2 or a TLR7 agonist.

Promotie