10 citations · 14 across the 7 of their papers we have counts for
4 papers · 1 filter
The Pitfalls of Benchmarking in Algorithm Selection: What We Are Getting Wrong
Gašper Petelin, Gjorgjina Cenikj
Algorithm selection, aiming to identify the best algorithm for a given problem, plays a pivotal role in continuous black-box optimization. A common approach involves representing o…
A Survey of Features Used for Representing Black-box Single-objective Continuous Optimization
Gjorgjina Cenikj, Ana Nikolikj, Gašper Petelin +3
This survey examines key advancements in designing features to represent optimization problem instances, algorithm instances, and their interactions within the context of single-ob…
Dealing with zero-inflated data: achieving SOTA with a two-fold machine learning approach
Jože M. Rožanec, Gašper Petelin, João Costa +5
In many cases, a machine learning model must learn to correctly predict a few data points with particular values of interest in a broader range of data where many target values are…
DynamoRep: Trajectory-Based Population Dynamics for Classification of Black-box Optimization Problems
Gjorgjina Cenikj, Gašper Petelin, Carola Doerr +2
The application of machine learning (ML) models to the analysis of optimization algorithms requires the representation of optimization problems using numerical features. These feat…