Carola Doerr
YOU?
Author Swipe
View article: Towards the genome-scale discovery of bivariate monotonic classifiers
Towards the genome-scale discovery of bivariate monotonic classifiers Open
View article: Multi-parameter Control for the (1+(λ, λ))-GA on OneMax via Deep Reinforcement Learning
Multi-parameter Control for the (1+(λ, λ))-GA on OneMax via Deep Reinforcement Learning Open
It is well known that evolutionary algorithms can benefit from dynamic choices of the key parameters that control their behavior, to adjust their search strategy to the different stages of the optimization process. A prominent example wher…
View article: Enhancing Parameter Control Policies with State Information
Enhancing Parameter Control Policies with State Information Open
Parameter control and dynamic algorithm configuration study how to dynamically choose suitable configurations of a parametrized algorithm during the optimization process. Despite being an intensively researched topic in evolutionary comput…
View article: Illuminating the Diversity-Fitness Trade-Off in Black-Box Optimization
Illuminating the Diversity-Fitness Trade-Off in Black-Box Optimization Open
In real-world applications, users often favor structurally diverse design choices over one high-quality solution. It is therefore important to consider more solutions that decision makers can compare and further explore based on additional…
View article: Geometric Learning in Black-Box Optimization: A GNN Framework for Algorithm Performance Prediction
Geometric Learning in Black-Box Optimization: A GNN Framework for Algorithm Performance Prediction Open
Automated algorithm performance prediction in numerical blackbox optimization often relies on problem characterizations, such as exploratory landscape analysis features. These features are typically used as inputs to machine learning model…
View article: Cascading CMA-ES Instances for Generating Input-diverse Solution Batches
Cascading CMA-ES Instances for Generating Input-diverse Solution Batches Open
International audience
View article: Analyzing Single-objective Black-box Optimization Algorithms Using the Empirical Attainment Function
Analyzing Single-objective Black-box Optimization Algorithms Using the Empirical Attainment Function Open
International audience
View article: On the Importance of Reward Design in Reinforcement Learning-based Dynamic Algorithm Configuration: A Case Study on OneMax with (1+(λ,λ))-GA
On the Importance of Reward Design in Reinforcement Learning-based Dynamic Algorithm Configuration: A Case Study on OneMax with (1+(λ,λ))-GA Open
International audience
View article: Constructing optimal star discrepancy sets
Constructing optimal star discrepancy sets Open
We provide in this paper a constructive proof of optimal star discrepancy values in dimension 2 for up to 21 points and up to 8 points in dimension 3. This extends work by White (Numer. Math. 27 (1976/77), no. 2, 157–164) for up to six po…
View article: Identification of Monotonically Classifying Pairs of Genes for Ordinal Disease Outcomes
Identification of Monotonically Classifying Pairs of Genes for Ordinal Disease Outcomes Open
In this study, we extend an existing classification method for identifying pairs of genes whose joint expression is associated with binary outcomes to ordinal multi-class outcomes, such as overall survival or disease progression. Our appro…
View article: Searching permutations for constructing uniformly distributed point sets
Searching permutations for constructing uniformly distributed point sets Open
Uniformly distributed point sets of low discrepancy are heavily used in experimental design and across a very wide range of applications such as numerical integration, computer graphics, and finance. Recent methods based on Graph Neural Ne…
View article: Beyond Landscape Analysis: DynamoRep Features For Capturing Algorithm-Problem Interaction In Single-Objective Continuous Optimization
Beyond Landscape Analysis: DynamoRep Features For Capturing Algorithm-Problem Interaction In Single-Objective Continuous Optimization Open
The representation of optimization problems and algorithms in terms of numerical features is a well-established tool for comparing optimization problem instances, for analyzing the behavior of optimization algorithms, and the quality of ex…
View article: Cascading CMA-ES Instances for Generating Input-diverse Solution Batches
Cascading CMA-ES Instances for Generating Input-diverse Solution Batches Open
Rather than obtaining a single good solution for a given optimization problem, users often seek alternative design choices, because the best-found solution may perform poorly with respect to additional objectives or constraints that are di…
View article: Synergies of Deep and Classical Exploratory Landscape Features for Automated Algorithm Selection
Synergies of Deep and Classical Exploratory Landscape Features for Automated Algorithm Selection Open
View article: MO-IOHinspector: Anytime Benchmarking of Multi-objective Algorithms Using IOHprofiler
MO-IOHinspector: Anytime Benchmarking of Multi-objective Algorithms Using IOHprofiler Open
View article: MO-IOHinspector: Anytime Benchmarking of Multi-Objective Algorithms using IOHprofiler
MO-IOHinspector: Anytime Benchmarking of Multi-Objective Algorithms using IOHprofiler Open
Benchmarking is one of the key ways in which we can gain insight into the strengths and weaknesses of optimization algorithms. In sampling-based optimization, considering the anytime behavior of an algorithm can provide valuable insights f…
View article: Selecting Pre-trained Models for Transfer Learning with Data-centric Meta-features
Selecting Pre-trained Models for Transfer Learning with Data-centric Meta-features Open
View article: Illuminating the Diversity-Fitness Trade-Off in Black-Box Optimization
Illuminating the Diversity-Fitness Trade-Off in Black-Box Optimization Open
In real-world applications, users often favor structurally diverse design choices over one high-quality solution. It is hence important to consider more solutions that decision makers can compare and further explore based on additional cri…
View article: Using Machine Learning Methods to Assess Module Performance Contribution in Modular Optimization Frameworks
Using Machine Learning Methods to Assess Module Performance Contribution in Modular Optimization Frameworks Open
Modular algorithm frameworks not only allow for combinations never tested in manually selected algorithm portfolios, but they also provide a structured approach to assess which algorithmic ideas are crucial for the observed performance of …
View article: Tight Runtime Bounds for Static Unary Unbiased Evolutionary Algorithms on Linear Functions
Tight Runtime Bounds for Static Unary Unbiased Evolutionary Algorithms on Linear Functions Open
View article: Transforming the Challenge of Constructing Low-Discrepancy Point Sets into a Permutation Selection Problem
Transforming the Challenge of Constructing Low-Discrepancy Point Sets into a Permutation Selection Problem Open
Low discrepancy point sets have been widely used as a tool to approximate continuous objects by discrete ones in numerical processes, for example in numerical integration. Following a century of research on the topic, it is still unclear h…
View article: Hybridizing Target- and SHAP-encoded Features for Algorithm Selection in Mixed-variable Black-box Optimization
Hybridizing Target- and SHAP-encoded Features for Algorithm Selection in Mixed-variable Black-box Optimization Open
Exploratory landscape analysis (ELA) is a well-established tool to characterize optimization problems via numerical features. ELA is used for problem comprehension, algorithm design, and applications such as automated algorithm selection a…
View article: Large-Scale Benchmarking of Metaphor-Based Optimization Heuristics
Large-Scale Benchmarking of Metaphor-Based Optimization Heuristics Open
The number of proposed iterative optimization heuristics is growing steadily, and with this growth, there have been many points of discussion within the wider community. One particular criticism that is raised towards many new algorithms i…
View article: Impact of Training Instance Selection on Automated Algorithm Selection Models for Numerical Black-box Optimization
Impact of Training Instance Selection on Automated Algorithm Selection Models for Numerical Black-box Optimization Open
The recently proposed MA-BBOB function generator provides a way to create numerical black-box benchmark problems based on the well-established BBOB suite. Initial studies on this generator highlighted its ability to smoothly transition bet…
View article: Generalization Ability of Feature-Based Performance Prediction Models: A Statistical Analysis Across Benchmarks
Generalization Ability of Feature-Based Performance Prediction Models: A Statistical Analysis Across Benchmarks Open
International audience
View article: MA-BBOB: A Problem Generator for Black-Box Optimization Using Affine Combinations and Shifts
MA-BBOB: A Problem Generator for Black-Box Optimization Using Affine Combinations and Shifts Open
Choosing a set of benchmark problems is often a key component of any empirical evaluation of iterative optimization heuristics. In continuous, single-objective optimization, several sets of problems have become widespread, including the we…
View article: Comparison of High-Dimensional Bayesian Optimization Algorithms on BBOB
Comparison of High-Dimensional Bayesian Optimization Algorithms on BBOB Open
Bayesian Optimization (BO) is a class of surrogate-based black-box optimization heuristics designed to efficiently locate high-quality solutions for problems that are expensive to evaluate, and therefore allow only small evaluation budgets…
View article: MA-BBOB - Reproducibility and Additional Data
MA-BBOB - Reproducibility and Additional Data Open
Reproducibility files for the paper: MA-BBOB: A Problem Generator for Black-Box Optimization Using Affine Combinations and Shifts This document details the reproduction steps for the paper "MA-BBOB: A Problem Generator for Black-Box Optimi…
View article: A Survey of Meta-features Used for Automated Selection of Algorithms for Black-box Single-objective Continuous Optimization
A Survey of Meta-features Used for Automated Selection of Algorithms for Black-box Single-objective Continuous Optimization Open
The selection of the most appropriate algorithm to solve a given problem instance, known as algorithm selection, is driven by the potential to capitalize on the complementary performance of different algorithms across sets of problem insta…
View article: Generalization Ability of Feature-based Performance Prediction Models: A Statistical Analysis across Benchmarks
Generalization Ability of Feature-based Performance Prediction Models: A Statistical Analysis across Benchmarks Open
This study examines the generalization ability of algorithm performance prediction models across various benchmark suites. Comparing the statistical similarity between the problem collections with the accuracy of performance prediction mod…