1 citations · 1 across the 4 of their papers we have counts for
4 papers
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…
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…
RF+clust for Leave-One-Problem-Out Performance Prediction
Ana Nikolikj, Carola Doerr, Tome Eftimov
Per-instance automated algorithm configuration and selection are gaining significant moments in evolutionary computation in recent years. Two crucial, sometimes implicit, ingredien…