1 citations · 1 across the 4 of their papers we have counts for
5 papers
Sampling on Random Subspaces under Limited Data in the Context of Exploratory Landscape Analysis
Iván Olarte Rodríguez, Anja Jankovic, Thomas Bäck +1
Classical space-filling designs often fail to provide reliable statistical results for Exploratory Landscape Analysis (ELA) when only limited evaluation budgets are available, as c…
Does Dimensionality Reduction via Random Projections Preserve Landscape Features?
Iván Olarte Rodríguez, Anja Jankovic, Thomas Bäck +1
Exploratory Landscape Analysis (ELA) provides numerical features for characterizing black-box optimization problems. In high-dimensional settings, however, ELA suffers from sparsit…
Optimization is Not Enough: Why Problem Formulation Deserves Equal Attention
Iván Olarte Rodríguez, Gokhan Serhat, Mariusz Bujny +3
Black-box optimization is increasingly used in engineering design problems where simulation-based evaluations are costly and gradients are unavailable. In this context, the optimiz…
MECHBench: A Set of Black-Box Optimization Benchmarks originated from Structural Mechanics
Iván Olarte Rodríguez, Maria Laura Santoni, Fabian Duddeck +3
Benchmarking is essential for developing and evaluating black-box optimization algorithms, providing a structured means to analyze their search behavior. Its effectiveness relies o…
Feasibility-Driven Trust Region Bayesian Optimization
Paolo Ascia, Elena Raponi, Thomas Bäck +1
Bayesian optimization is a powerful tool for solving real-world optimization tasks under tight evaluation budgets, making it well-suited for applications involving costly simulatio…