3 papers
cs.AI2026
LLM Driven Design of Continuous Optimization Problems with Controllable High-level Properties
Urban Skvorc, Niki van Stein, Moritz Seiler +3
Benchmarking in continuous black-box optimisation is hindered by the limited structural diversity of existing test suites such as BBOB. We explore whether large language models emb…
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…