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20222024
most citedA Collection of Deep Learning-based Feature-Free Approaches for Characterizing Single-Objective Continuous Fitness Landscapes

1 citations · 2 across the 5 of their papers we have counts for

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cs.NE2024

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…

cs.NE2024

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…