85 citations · 897 across the 89 of their papers we have counts for
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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…
OPTION: OPTImization Algorithm Benchmarking ONtology
Ana Kostovska, Diederick Vermetten, Carola Doerr +3
Many platforms for benchmarking optimization algorithms offer users the possibility of sharing their experimental data with the purpose of promoting reproducible and reusable resea…
Personalizing Performance Regression Models to Black-Box Optimization Problems
Tome Eftimov, Anja Jankovic, Gorjan Popovski +2
Accurately predicting the performance of different optimization algorithms for previously unseen problem instances is crucial for high-performing algorithm selection and configurat…
The Impact of Hyper-Parameter Tuning for Landscape-Aware Performance Regression and Algorithm Selection
Anja Jankovic, Gorjan Popovski, Tome Eftimov +1
Automated algorithm selection and configuration methods that build on exploratory landscape analysis (ELA) are becoming very popular in Evolutionary Computation. However, despite a…
Tuning as a Means of Assessing the Benefits of New Ideas in Interplay with Existing Algorithmic Modules
Jacob de Nobel, Diederick Vermetten, Hao Wang +2
Introducing new algorithmic ideas is a key part of the continuous improvement of existing optimization algorithms. However, when introducing a new component into an existing algori…