82 citations · 247 across the 54 of their papers we have counts for
5 papers · 2 filters
TransOpt: Transformer-based Representation Learning for Optimization Problem Classification
Gjorgjina Cenikj, Gašper Petelin, Tome Eftimov
We propose a representation of optimization problem instances using a transformer-based neural network architecture trained for the task of problem classification of the 24 problem…
PS-AAS: Portfolio Selection for Automated Algorithm Selection in Black-Box Optimization
Ana Kostovska, Gjorgjina Cenikj, Diederick Vermetten +6
The performance of automated algorithm selection (AAS) strongly depends on the portfolio of algorithms to choose from. Selecting the portfolio is a non-trivial task that requires b…
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…
Assessing the Generalizability of a Performance Predictive Model
Ana Nikolikj, Gjorgjina Cenikj, Gordana Ispirova +6
A key component of automated algorithm selection and configuration, which in most cases are performed using supervised machine learning (ML) methods is a good-performing predictive…
Sensitivity Analysis of RF+clust for Leave-one-problem-out Performance Prediction
Ana Nikolikj, Michal Pluháček, Carola Doerr +2
Leave-one-problem-out (LOPO) performance prediction requires machine learning (ML) models to extrapolate algorithms' performance from a set of training problems to a previously uns…