82 citations · 247 across the 54 of their papers we have counts for
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Explainable Landscape-Aware Optimization Performance Prediction
Risto Trajanov, Stefan Dimeski, Martin Popovski +2
Efficient solving of an unseen optimization problem is related to appropriate selection of an optimization algorithm and its hyper-parameters. For this purpose, automated algorithm…
FoodChem: A food-chemical relation extraction model
Gjorgjina Cenikj, Barbara Koroušić Seljak, Tome Eftimov
In this paper, we present FoodChem, a new Relation Extraction (RE) model for identifying chemicals present in the composition of food entities, based on textual information provide…
A Complementarity Analysis of the COCO Benchmark Problems and Artificially Generated Problems
Urban Škvorc, Tome Eftimov, Peter Korošec
When designing a benchmark problem set, it is important to create a set of benchmark problems that are a good generalization of the set of all possible problems. One possible way o…
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…