1 citations · 2 across the 5 of their papers we have counts for
5 papers
Hybridizing Target- and SHAP-encoded Features for Algorithm Selection in Mixed-variable Black-box Optimization
Konstantin Dietrich, Raphael Patrick Prager, Carola Doerr +1
Exploratory landscape analysis (ELA) is a well-established tool to characterize optimization problems via numerical features. ELA is used for problem comprehension, algorithm desig…
Exploratory Landscape Analysis for Mixed-Variable Problems
Raphael Patrick Prager, Heike Trautmann
Exploratory landscape analysis and fitness landscape analysis in general have been pivotal in facilitating problem understanding, algorithm design and endeavors such as automated a…
Improving Automated Algorithm Selection by Advancing Fitness Landscape Analysis
Raphael Patrick Prager
Optimization is ubiquitous in our daily lives. In the past, (sub-)optimal solutions to any problem have been derived by trial and error, sheer luck, or the expertise of knowledgeab…
HPO X ELA: Investigating Hyperparameter Optimization Landscapes by Means of Exploratory Landscape Analysis
Lennart Schneider, Lennart Schäpermeier, Raphael Patrick Prager +3
Hyperparameter optimization (HPO) is a key component of machine learning models for achieving peak predictive performance. While numerous methods and algorithms for HPO have been p…
A Collection of Deep Learning-based Feature-Free Approaches for Characterizing Single-Objective Continuous Fitness Landscapes
Moritz Vinzent Seiler, Raphael Patrick Prager, Pascal Kerschke +1
Exploratory Landscape Analysis is a powerful technique for numerically characterizing landscapes of single-objective continuous optimization problems. Landscape insights are crucia…