3 citations · 5 across the 5 of their papers we have counts for
6 papers
Unsupervised Multi-kernel Learning for Automated Algorithm Selection
Yihang Lu, Tome Eftimov, Carola Doerr
Automated algorithm selection in black-box optimization typically relies on supervised models that map landscape features to algorithm performance labels. Such models are costly to…
Geometric Learning in Black-Box Optimization: A GNN Framework for Algorithm Performance Prediction
Ana Kostovska, Carola Doerr, Sašo Džeroski +2
Automated algorithm performance prediction in numerical blackbox optimization often relies on problem characterizations, such as exploratory landscape analysis features. These feat…
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…
Generalization Ability of Feature-based Performance Prediction Models: A Statistical Analysis across Benchmarks
Ana Nikolikj, Ana Kostovska, Gjorgjina Cenikj +2
This study examines the generalization ability of algorithm performance prediction models across various benchmark suites. Comparing the statistical similarity between the problem…
Quantifying Individual and Joint Module Impact in Modular Optimization Frameworks
Ana Nikolikj, Ana Kostovska, Diederick Vermetten +2
This study explores the influence of modules on the performance of modular optimization frameworks for continuous single-objective black-box optimization. There is an extensive var…
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…