Diederick Vermetten | DeepAI

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∙ 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. ∙

∙ 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. ∙

∙ 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. ∙

∙ 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. ∙

∙ 05/24/2023

Challenges of ELA-guided Function Evolution using Genetic Programming

Within the optimization community, the question of how to generate new o...

0 Fu Xing Long, et al. ∙

∙ 05/24/2023

Analysis of modular CMA-ES on strict box-constrained problems in the SBOX-COST benchmarking suite

Box-constraints limit the domain of decision variables and are common in...

0 Diederick Vermetten, et al. ∙

∙ 04/25/2023

When to be Discrete: Analyzing Algorithm Performance on Discretized Continuous Problems

The domain of an optimization problem is seen as one of its most importa...

0 André Thomaser, et al. ∙

∙ 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. ∙

∙ 02/17/2023

To Switch or not to Switch: Predicting the Benefit of Switching between Algorithms based on Trajectory Features

Dynamic algorithm selection aims to exploit the complementarity of multi...

0 Diederick Vermetten, et al. ∙

∙ 02/02/2023

Benchmarking Algorithms for Submodular Optimization Problems Using IOHProfiler

Submodular functions play a key role in the area of optimization as they...

0 Frank Neumann, et al. ∙

∙ 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. ∙

∙ 11/29/2022

BBOB Instance Analysis: Landscape Properties and Algorithm Performance across Problem Instances

Benchmarking is a key aspect of research into optimization algorithms, a...

0 Fu Xing Long, et al. ∙

∙ 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. ∙

∙ 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. ∙

∙ 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. ∙

∙ 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. ∙

∙ 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. ∙

∙ 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. ∙

∙ 09/20/2021

Benchmarking the Status of Default Pseudorandom Number Generators in Common Programming Languages

The ever-increasing need for random numbers is clear in many areas of co...

0 Nils van den Honert, et al. ∙

∙ 05/10/2021

Is there Anisotropy in Structural Bias?

Structural Bias (SB) is an important type of algorithmic deficiency with...

7 Diederick Vermetten, et al. ∙

∙ 04/24/2021

OPTION: OPTImization Algorithm Benchmarking ONtology

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

0 Ana Kostovska, et al. ∙

∙ 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. ∙

∙ 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. ∙

∙ 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. ∙

∙ 06/11/2020

Towards Dynamic Algorithm Selection for Numerical Black-Box Optimization: Investigating BBOB as a Use Case

One of the most challenging problems in evolutionary computation is to s...

3 Diederick Vermetten, et al. ∙

∙ 12/12/2019

Sequential vs. Integrated Algorithm Selection and Configuration: A Case Study for the Modular CMA-ES

When faced with a specific optimization problem, choosing which algorith...

15 Diederick Vermetten, et al. ∙

∙ 04/16/2019

Online Selection of CMA-ES Variants

In the field of evolutionary computation, one of the most challenging to...

0 Diederick Vermetten, et al. ∙

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