63 citations · 185 across the 20 of their papers we have counts for
20 papers
Block-Bench: A Framework for Controllable and Transparent Discrete Optimization Benchmarking
Furong Ye, Frank Neumann, Thomas Bäck +1
We present a novel approach for constructing discrete optimization benchmarks that enables fine-grained control over problem properties, and such benchmarks can facilitate analyzin…
From Heuristic Selection to Automated Algorithm Design: LLMs Benefit from Strong Priors
Qi Huang, Furong Ye, Ananta Shahane +2
Large Language Models (LLMs) have already been widely adopted for automated algorithm design, demonstrating strong abilities in generating and evolving algorithms across various fi…
Log-normal Mutations and their Use in Detecting Surreptitious Fake Images
Ismail Labiad, Thomas Bäck, Pierre Fernandez +5
In many cases, adversarial attacks are based on specialized algorithms specifically dedicated to attacking automatic image classifiers. These algorithms perform well, thanks to an…
Impact of spatial transformations on landscape features of CEC2022 basic benchmark problems
Haoran Yin, Diederick Vermetten, Furong Ye +2
When benchmarking optimization heuristics, we need to take care to avoid an algorithm exploiting biases in the construction of the used problems. One way in which this might be don…
MA-BBOB: A Problem Generator for Black-Box Optimization Using Affine Combinations and Shifts
Diederick Vermetten, Furong Ye, Thomas Bäck +1
Choosing a set of benchmark problems is often a key component of any empirical evaluation of iterative optimization heuristics. In continuous, single-objective optimization, severa…
MA-BBOB: Many-Affine Combinations of BBOB Functions for Evaluating AutoML Approaches in Noiseless Numerical Black-Box Optimization Contexts
Diederick Vermetten, Furong Ye, Thomas Bäck +1
Extending a recent suggestion to generate new instances for numerical black-box optimization benchmarking by interpolating pairs of the well-established BBOB functions from the COm…
When to be Discrete: Analyzing Algorithm Performance on Discretized Continuous Problems
André Thomaser, Jacob de Nobel, Diederick Vermetten +3
The domain of an optimization problem is seen as one of its most important characteristics. In particular, the distinction between continuous and discrete optimization is rather im…
Using Affine Combinations of BBOB Problems for Performance Assessment
Diederick Vermetten, Furong Ye, Carola Doerr
Benchmarking plays a major role in the development and analysis of optimization algorithms. As such, the way in which the used benchmark problems are defined significantly affects…
Towards Self-adaptive Mutation in Evolutionary Multi-Objective Algorithms
Furong Ye, Frank Neumann, Jacob de Nobel +2
Parameter control has succeeded in accelerating the convergence process of evolutionary algorithms. While empirical and theoretical studies have shed light on the behavior of algor…
Benchmarking Algorithms for Submodular Optimization Problems Using IOHProfiler
Frank Neumann, Aneta Neumann, Chao Qian +7
Submodular functions play a key role in the area of optimization as they allow to model many real-world problems that face diminishing returns. Evolutionary algorithms have been sh…
Non-Elitist Selection Can Improve the Performance of Irace
Furong Ye, Diederick L. Vermetten, Carola Doerr +1
Modern optimization strategies such as evolutionary algorithms, ant colony algorithms, Bayesian optimization techniques, etc. come with several parameters that steer their behavior…
IOHexperimenter: Benchmarking Platform for Iterative Optimization Heuristics
Jacob de Nobel, Furong Ye, Diederick Vermetten +3
We present IOHexperimenter, the experimentation module of the IOHprofiler project, which aims at providing an easy-to-use and highly customizable toolbox for benchmarking iterative…
Automated Configuration of Genetic Algorithms by Tuning for Anytime Performance
Furong Ye, Carola Doerr, Hao Wang +1
Finding the best configuration of algorithms' hyperparameters for a given optimization problem is an important task in evolutionary computation. We compare in this work the results…
Leveraging Benchmarking Data for Informed One-Shot Dynamic Algorithm Selection
Furong Ye, Carola Doerr, Thomas Bäck
A key challenge in the application of evolutionary algorithms in practice is the selection of an algorithm instance that best suits the problem at hand. What complicates this decis…
IOHanalyzer: Detailed Performance Analyses for Iterative Optimization Heuristics
Hao Wang, Diederick Vermetten, Furong Ye +2
Benchmarking and performance analysis play an important role in understanding the behaviour of iterative optimization heuristics (IOHs) such as local search algorithms, genetic and…
Benchmarking a Genetic Algorithm with Configurable Crossover Probability
Furong Ye, Hao Wang, Carola Doerr +1
We investigate a family of Genetic Algorithms (GAs) which creates offspring either from mutation or by recombining two randomly chosen parents. By scaling the crossover pro…
Benchmarking Discrete Optimization Heuristics with IOHprofiler
Carola Doerr, Furong Ye, Naama Horesh +3
Automated benchmarking environments aim to support researchers in understanding how different algorithms perform on different types of optimization problems. Such comparisons provi…
Interpolating Local and Global Search by Controlling the Variance of Standard Bit Mutation
Furong Ye, Carola Doerr, Thomas Bäck
A key property underlying the success of evolutionary algorithms (EAs) is their global search behavior, which allows the algorithms to `jump' from a current state to other parts of…
IOHprofiler: A Benchmarking and Profiling Tool for Iterative Optimization Heuristics
Carola Doerr, Hao Wang, Furong Ye +2
IOHprofiler is a new tool for analyzing and comparing iterative optimization heuristics. Given as input algorithms and problems written in C or Python, it provides as output a stat…
Towards a Theory-Guided Benchmarking Suite for Discrete Black-Box Optimization Heuristics: Profiling EA Variants on OneMax and LeadingOnes
Carola Doerr, Furong Ye, Sander van Rijn +2
Theoretical and empirical research on evolutionary computation methods complement each other by providing two fundamentally different approaches towards a better understanding of b…