b'Carola Doerr'

research

∙ 06/30/2023

Comparing Algorithm Selection Approaches on Black-Box Optimization Problems

Performance complementarity of solvers available to tackle black-box opt...

0 Ana Kostovska, et al. ∙

research

∙ 06/29/2023

Computing Star Discrepancies with Numerical Black-Box Optimization Algorithms

The L_∞ star discrepancy is a measure for the regularity of a finite set...

0 Francois Clément, et al. ∙

research

∙ 06/27/2023

Heuristic Approaches to Obtain Low-Discrepancy Point Sets via Subset Selection

Building upon the exact methods presented in our earlier work [J. Comple...

0 Francois Clément, et al. ∙

research

∙ 06/18/2023

MA-BBOB: Many-Affine Combinations of BBOB Functions for Evaluating AutoML Approaches in Noiseless Numerical Black-Box Optimization Contexts

Extending a recent suggestion to generate new instances for numerical bl...

0 Diederick Vermetten, et al. ∙

research

∙ 06/08/2023

DynamoRep: Trajectory-Based Population Dynamics for Classification of Black-box Optimization Problems

The application of machine learning (ML) models to the analysis of optim...

0 Gjorgjina Cenikj, et al. ∙

research

∙ 06/07/2023

Self-Adjusting Weighted Expected Improvement for Bayesian Optimization

Bayesian Optimization (BO) is a class of surrogate-based, sample-efficie...

0 Carolin Benjamins, et al. ∙

research

∙ 06/01/2023

Algorithm Instance Footprint: Separating Easily Solvable and Challenging Problem Instances

In black-box optimization, it is essential to understand why an algorith...

0 Ana Nikolikj, et al. ∙

research

∙ 05/31/2023

Assessing the Generalizability of a Performance Predictive Model

A key component of automated algorithm selection and configuration, whic...

0 Ana Nikolikj, et al. ∙

research

∙ 05/30/2023

Sensitivity Analysis of RF+clust for Leave-one-problem-out Performance Prediction

Leave-one-problem-out (LOPO) performance prediction requires machine lea...

0 Ana Nikolikj, et al. ∙

research

∙ 03/08/2023

Using Affine Combinations of BBOB Problems for Performance Assessment

Benchmarking plays a major role in the development and analysis of optim...

0 Diederick Vermetten, et al. ∙

research

∙ 03/02/2023

Comparison of High-Dimensional Bayesian Optimization Algorithms on BBOB

Bayesian Optimization (BO) is a class of black-box, surrogate-based heur...

0 Maria Laura Santoni, et al. ∙

research

∙ 02/23/2023

Tight Runtime Bounds for Static Unary Unbiased Evolutionary Algorithms on Linear Functions

In a seminal paper in 2013, Witt showed that the (1+1) Evolutionary Algo...

0 Carola Doerr, et al. ∙

research

∙ 02/23/2023

Using Automated Algorithm Configuration for Parameter Control

Dynamic Algorithm Configuration (DAC) tackles the question of how to aut...

0 Deyao Chen, et al. ∙

research

∙ 01/24/2023

Using Knowledge Graphs for Performance Prediction of Modular Optimization Algorithms

Empirical data plays an important role in evolutionary computation resea...

0 Ana Kostovska, et al. ∙

research

∙ 01/23/2023

RF+clust for Leave-One-Problem-Out Performance Prediction

Per-instance automated algorithm configuration and selection are gaining...

0 Ana Nikolikj, et al. ∙

research

∙ 11/21/2022

Explainable Model-specific Algorithm Selection for Multi-Label Classification

Multi-label classification (MLC) is an ML task of predictive modeling in...

0 Ana Kostovska, et al. ∙

research

∙ 11/17/2022

Towards Automated Design of Bayesian Optimization via Exploratory Landscape Analysis

Bayesian optimization (BO) algorithms form a class of surrogate-based he...

0 Carolin Benjamins, et al. ∙

research

∙ 11/02/2022

PI is back! Switching Acquisition Functions in Bayesian Optimization

Bayesian Optimization (BO) is a powerful, sample-efficient technique to ...

0 Carolin Benjamins, et al. ∙

research

∙ 09/09/2022

Improving Nevergrad's Algorithm Selection Wizard NGOpt through Automated Algorithm Configuration

Algorithm selection wizards are effective and versatile tools that autom...

0 Risto Trajanov, et al. ∙

research

∙ 09/09/2022

Fast Re-Optimization of LeadingOnes with Frequent Changes

In real-world optimization scenarios, the problem instance that we are a...

0 Nina Bulanova, et al. ∙

research

∙ 05/07/2022

Automated Algorithm Selection for Radar Network Configuration

The configuration of radar networks is a complex problem that is often p...

0 Quentin Renau, et al. ∙

research

∙ 04/28/2022

High Dimensional Bayesian Optimization with Kernel Principal Component Analysis

Bayesian Optimization (BO) is a surrogate-based global optimization stra...

11 Kirill Antonov, et al. ∙

research

∙ 04/27/2022

Run Time Analysis for Random Local Search on Generalized Majority Functions

Run time analysis of evolutionary algorithms recently makes significant ...

0 Carola Doerr, et al. ∙

research

∙ 04/25/2022

SELECTOR: Selecting a Representative Benchmark Suite for Reproducible Statistical Comparison

Fair algorithm evaluation is conditioned on the existence of high-qualit...

0 Gjorgjina Cenikj, et al. ∙

research

∙ 04/20/2022

Per-run Algorithm Selection with Warm-starting using Trajectory-based Features

Per-instance algorithm selection seeks to recommend, for a given problem...

4 Ana Kostovska, et al. ∙

research

∙ 04/20/2022

Analyzing the Impact of Undersampling on the Benchmarking and Configuration of Evolutionary Algorithms

The stochastic nature of iterative optimization heuristics leads to inhe...

10 Diederick Vermetten, et al. ∙

research

∙ 04/15/2022

The Importance of Landscape Features for Performance Prediction of Modular CMA-ES Variants

Selecting the most suitable algorithm and determining its hyperparameter...

14 Ana Kostovska, et al. ∙

research

∙ 04/13/2022

Chaining of Numerical Black-box Algorithms: Warm-Starting and Switching Points

Dynamic algorithm selection can be beneficial for solving numerical blac...

27 Dominik Schröder, et al. ∙

research

∙ 04/13/2022

Trajectory-based Algorithm Selection with Warm-starting

Landscape-aware algorithm selection approaches have so far mostly been r...

2 Anja Jankovic, et al. ∙

research

∙ 03/17/2022

Non-Elitist Selection among Survivor Configurations can Improve the Performance of Irace

Modern optimization strategies such as evolutionary algorithms, ant colo...

0 Furong Ye, et al. ∙

research

∙ 02/07/2022

Theory-inspired Parameter Control Benchmarks for Dynamic Algorithm Configuration

It has long been observed that the performance of evolutionary algorithm...

0 André Biedenkapp, et al. ∙

research

∙ 11/07/2021

IOHexperimenter: Benchmarking Platform for Iterative Optimization Heuristics

We present IOHexperimenter, the experimentation module of the IOHprofile...

0 Jacob de Nobel, et al. ∙

research

∙ 06/11/2021

Automated Configuration of Genetic Algorithms by Tuning for Anytime Performance

Finding the best configuration of algorithms' hyperparameters for a give...

13 Furong Ye, et al. ∙

research

∙ 04/24/2021

OPTION: OPTImization Algorithm Benchmarking ONtology

Many platforms for benchmarking optimization algorithms offer users the ...

0 Ana Kostovska, et al. ∙

research

∙ 04/22/2021

Personalizing Performance Regression Models to Black-Box Optimization Problems

Accurately predicting the performance of different optimization algorith...

0 Tome Eftimov, et al. ∙

research

∙ 04/19/2021

The Impact of Hyper-Parameter Tuning for Landscape-Aware Performance Regression and Algorithm Selection

Automated algorithm selection and configuration methods that build on ex...

6 Anja Jankovic, et al. ∙

research

∙ 02/25/2021

Tuning as a Means of Assessing the Benefits of New Ideas in Interplay with Existing Algorithmic Modules

Introducing new algorithmic ideas is a key part of the continuous improv...

6 Jacob de Nobel, et al. ∙

research

∙ 02/23/2021

Blending Dynamic Programming with Monte Carlo Simulation for Bounding the Running Time of Evolutionary Algorithms

With the goal to provide absolute lower bounds for the best possible run...

0 Kirill Antonov, et al. ∙

research

∙ 02/12/2021

Leveraging Benchmarking Data for Informed One-Shot Dynamic Algorithm Selection

A key challenge in the application of evolutionary algorithms in practic...

0 Furong Ye, et al. ∙

research

∙ 02/12/2021

Towards Large Scale Automated Algorithm Design by Integrating Modular Benchmarking Frameworks

We present a first proof-of-concept use-case that demonstrates the effic...

0 Amine Aziz-Alaoui, et al. ∙

research

∙ 02/10/2021

Towards Feature-Based Performance Regression Using Trajectory Data

Black-box optimization is a very active area of research, with many new ...

0 Anja Jankovic, et al. ∙

research

∙ 02/09/2021

Optimal Static Mutation Strength Distributions for the (1+λ) Evolutionary Algorithm on OneMax

Most evolutionary algorithms have parameters, which allow a great flexib...

0 Maxim Buzdalov, et al. ∙

research

∙ 02/01/2021

Towards Explainable Exploratory Landscape Analysis: Extreme Feature Selection for Classifying BBOB Functions

Facilitated by the recent advances of Machine Learning (ML), the automat...

0 Quentin Renau, et al. ∙

research

∙ 01/19/2021

Star Discrepancy Subset Selection: Problem Formulation and Efficient Approaches for Low Dimensions

Motivated by applications in instance selection, we introduce the star d...

0 Carola Doerr, et al. ∙

research

∙ 12/15/2020

Squirrel: A Switching Hyperparameter Optimizer

In this short note, we describe our submission to the NeurIPS 2020 BBO c...

26 Noor Awad, et al. ∙

research

∙ 10/08/2020

Black-Box Optimization Revisited: Improving Algorithm Selection Wizards through Massive Benchmarking

Existing studies in black-box optimization suffer from low generalizabil...

0 Laurent Meunier, et al. ∙

research

∙ 09/30/2020

Linear Matrix Factorization Embeddings for Single-objective Optimization Landscapes

Automated per-instance algorithm selection and configuration have shown ...

0 Tome Eftimov, et al. ∙

research

∙ 07/08/2020

IOHanalyzer: Performance Analysis for Iterative Optimization Heuristic

We propose IOHanalyzer, a new software for analyzing the empirical perfo...

4 Hao Wang, et al. ∙

research

∙ 07/07/2020

Benchmarking in Optimization: Best Practice and Open Issues

This survey compiles ideas and recommendations from more than a dozen re...

11 Thomas Bartz-Beielstein, et al. ∙

research

∙ 07/02/2020

High Dimensional Bayesian Optimization Assisted by Principal Component Analysis

Bayesian Optimization (BO) is a surrogate-assisted global optimization t...

26 Elena Raponi, et al. ∙

Carola Doerr

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