document

Synthetic data generation for the optimization of strains in metabolic engineering using latent space representations derived from a Conditional Variational Autoencoder

Alwani, Neil (author)

This study investigates the application of generative models for synthetic data generation in pathway optimization experiments within the field of metabolic engineering. Conditional Variational Autoencoders (CVAEs) use neural networks and latent variable distributions to generate new, plausible data samples. We adapt this model by conditioning...

bachelor thesis 2024

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Generalized Model and Deep Reinforcement Learning-Based Evolutionary Method for Multitype Satellite Observation Scheduling

Song, Yanjie (author), Ou, Junwei (author), Pedrycz, Witold (author), Suganthan, Ponnuthurai Nagaratnam (author), Wang, X. (author), Xing, Lining (author), Zhang, Yue (author)

Multitype satellite observation, including optical observation satellites, synthetic aperture radar (SAR) satellites, and electromagnetic satellites, has become an important direction in integrated satellite applications due to its ability to cope with various complex situations. In the multitype satellite observation scheduling problem ...

journal article 2024

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Sustainable urban logistics: A case study of waterway integration in Amsterdam

Pourmohammadzia, N. (author), van Koningsveld, M. (author)

This paper tackles the growing challenges in urban logistics by presenting an optimal distribution network that integrates urban waterways and last-mile delivery, tailored for cities boasting extensive waterway networks. We examine Amsterdam's city center as a case study, prompted by the strain on quay walls, congestion, and emissions, urging...

journal article 2024

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Optimal Robust Decision Trees: A dynamic programming approach

Bien, Benedict (author)

Decision trees are integral to machine learning, with their robustness being a critical measure of effectiveness against adversarial data manipulations. Despite advancements in algorithms, current solutions are either optimal but lack scalability or scale well, but do not guarrantee optimality. This paper presents a novel adaptation of the...

bachelor thesis 2023

document

User Interface Optimization for Quantified Self

Bangar, Yugdeep (author)

Self-tracking has expanded exponentially in an era defined by the ubiquitous presence of wearable technologies and smart devices. From health and fitness to finances and productivity, these devices empower users to delve into their quantified self (QS) through an almost infinite amount of visualizations. However, a user has limited time to...

master thesis 2023

document

QAOA Mixing Hamiltonians for MinVertexCover

Driebergen, Tim (author)

The minimum vertex cover problem (MinVertexCover) is an important optimization problem in graph theory, with applications in numerous fields outside of mathematics. As MinVertexCover is an NP-hard problem, there currently exists no efficient algorithm to find an optimal solution on arbitrary graphs. We consider quantum optimization algorithms,...

master thesis 2023

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Pattern QUBOs: Algorithmic Construction of 3SAT-to-QUBO Transformations

Zielinski, Sebastian (author), Nüßlein, Jonas (author), Stein, Jonas (author), Gabor, Thomas (author), Linnhoff-Popien, Claudia (author), Feld, S. (author)

One way of solving 3sat instances on a quantum computer is to transform the 3sat instances into instances of Quadratic Unconstrained Binary Optimizations (QUBOs), which can be used as an input for the QAOA algorithm on quantum gate systems or as an input for quantum annealers. This mapping is performed by a 3sat-to-QUBO transformation....

journal article 2023

document

A hybrid deep-learning-metaheuristic framework for bi-level network design problems

Madadi, B. (author), Correia, Gonçalo (author)

This study proposes a hybrid deep-learning-metaheuristic framework with a bi-level architecture for road network design problems (NDPs). We train a graph neural network (GNN) to approximate the solution of the user equilibrium (UE) traffic assignment problem and use inferences made by the trained model to calculate fitness function...

journal article 2023

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The first AI4TSP competition: Learning to solve stochastic routing problems

Zhang, Yingqian (author), Bliek, Laurens (author), da Costa, Paulo (author), Refaei Afshar, Reza (author), Reijnen, Robbert (author), Catshoek, T. (author), Vos, D.A. (author), Verwer, S.E. (author), Schmitt-Ulms, Fynn (author)

This paper reports on the first international competition on AI for the traveling salesman problem (TSP) at the International Joint Conference on Artificial Intelligence 2021 (IJCAI-21). The TSP is one of the classical combinatorial optimization problems, with many variants inspired by real-world applications. This first competition asked the...

journal article 2023

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Predict+optimize for combinatorial optimization with uncertainty in the constraints

van Steijn, Jeroen (author)

In this work, it is investigated whether the predict+optimize framework could be utilized for combinatorial optimization problems with a linear objective that have uncertainty in the constraint parameters, such that it outperforms prediction-error-based training. To this end, a predict+optimize formulation of the 0-1 knapsack problem is used,...

master thesis 2022

document

SAT-based optimisation for the resource-constrained project scheduling problem with time-dependent resource capacities and requests

Pleunes, Jelle (author)

In this paper, a variant of the resource-constrained project scheduling problem is discussed. This variant introduces time-dependence for resource capacities and requests, making the problem a more realistic model for many practical applications such as production scheduling and medical research project planning. The main aim of this paper is to...

bachelor thesis 2022

document

A Lagrangian decomposition scheme for choice-based optimization

Pacheco Paneque, Meritxell (author), Gendron, Bernard (author), Sharif Azadeh, S. (author), Bierlaire, Michel (author)

Choice-based optimization problems are the family of optimization problems that incorporate the stochasticity of individual preferences according to discrete choice models to make planning decisions. This integration brings non-convexity and nonlinearity to the associated mathematical formulations. Previously, the authors have tackled these...

journal article 2022

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An overview of structural systems theory

Ramos, Guilherme (author), Aguiar, A. Pedro (author), Gonçalves Melo Pequito, S.D. (author)

This paper provides an overview of the research conducted in the context of structural (or structured) systems. These are parametrized models used to assess and design system theoretical properties without considering a specific realization of the parameters (which could be uncertain or unknown). The research in structural systems led to a...

journal article 2022

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Quantum Annealing for Seismic Imaging: Exploring Quantum Annealing Possibilities For Residual Statics Estimation using the D-Wave Advantage System and Hybrid Solver

van der Linde, Stan (author)

Recent developments in quantum annealing have shown promising results in logistics, life sciences, machine learning and more. However, in the field of geophysical sciences the applications have been limited. A quantum annealing application was developed for residual statics estimation. Residual statics estimation is a highly non-linear problem...

master thesis 2021

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A novel surrogate-assisted evolutionary algorithm applied to partition-based ensemble learning

Dushatskiy, A. (author), Alderliesten, T. (author), Bosman, P.A.N. (author)

We propose a novel surrogate-assisted Evolutionary Algorithm for solving expensive combinatorial optimization problems. We integrate a surrogate model, which is used for fitness value estimation, into a state-of-the-art P3-like variant of the Gene-Pool Optimal Mixing Algorithm (GOMEA) and adapt the resulting algorithm for solving non-binary...

conference paper 2021

document

Continuous Surrogate-Based Optimization Algorithms Are Well-Suited for Expensive Discrete Problems

Karlsson, R.K.A. (author), Bliek, L. (author), Verwer, S.E. (author), de Weerdt, M.M. (author)

One method to solve expensive black-box optimization problems is to use a surrogate model that approximates the objective based on previous observed evaluations. The surrogate, which is cheaper to evaluate, is optimized instead to find an approximate solution to the original problem. In the case of discrete problems, recent research has...

conference paper 2021

document

An Investigation Into Predict-and-Optimize Machine Learning

Puppels, Thomas (author)

Predict-and-Optimize (PnO) is a relatively new machine learning paradigm that has attracted recent interest: it concerns the prediction of parameters that determine the value of solutions to an optimization problem, such that the optimizer ends up picking a good solution. Training estimators with standard loss functions like mean squared error...

master thesis 2020

document

Deepification: Learning Variable Ordering Heuristics in Constraint Optimization Problems

Doolaard, F.P. (author)

Constraint programming is a paradigm for solving combinatorial problems by checking whether constraints are satisfied in a constraint satisfaction problem or by optimizing an objective in a constraint optimization problem. To find solutions, the solver needs to find a variable and value ordering. Numerous heuristics designed by human experts...

master thesis 2020

document

Optimizing Memory Mapping for Dataflow Inference Accelerators: Efficient Memory Utilization on FPGAs

Kroes, Mairin (author)

Convolutional Neural Network (CNN) inference has gained a significant amount of traction for performing tasks like speech recognition and image classification. To improve the accuracy with which these tasks can be performed, CNNs are typically designed to be deep, encompassing a large number of neural network layers. As a result, the...

master thesis 2020

document

Learning variable selection rules for the branch-and-bound algorithm using reinforcement learning

Scavuzzo Montana, Lara (author)

Mixed Integer Linear Programming (MILP) is a generalization of classical linear programming where we restrict some (or all) variables to take integer values. Numerous real-world problems can be modeled as MILPs, such as production planning, scheduling, network design optimization and many more. MILPs are, in fact, NP-hard. State-of-the-art...

master thesis 2020