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

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

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.

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

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

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.

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

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

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

Promotor: Prof.dr.ir. E. Deprettere

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

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

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.

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

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.

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.

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

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

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.

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.

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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…

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…

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

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.

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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…

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…

Electrocatalysis allows for storing electricity or converting it into chemical bonds, producing chemical building blocks and fuels using renewable resources.

Promotor: Prof.dr. K.J. Batenburg

Promotor: M.A.G.J. Orrit