85 citations · 897 across the 89 of their papers we have counts for
104 papers
The Modular CMA-ES: A Framework for Modern Evolution Strategies
Jacob de Nobel, Diederick Vermetten, Anna V. Kononova +2
Since their introduction, modern evolution strategies, such as the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), have become established as powerful methods for continu…
AdaEva: Accelerating LLM-Driven Algorithm Design with Adaptive Partial Evaluation
Tai Nguyen, Fei Liu, Phong Le +2
Large Language Models (LLMs) are increasingly used for automated algorithm design. However the computational cost of evaluating the generated algorithms can be excessive. We consid…
Comparing Algorithm Selection Approaches on Black-Box Optimization Problems
Ana Kostovska, Anja Jankovic, Diederick Vermetten +3
Performance complementarity of solvers available to tackle black-box optimization problems gives rise to the important task of algorithm selection (AS). Automated AS approaches can…
Self-Adjusting Weighted Expected Improvement for Bayesian Optimization
Carolin Benjamins, Elena Raponi, Anja Jankovic +2
Bayesian Optimization (BO) is a class of surrogate-based, sample-efficient algorithms for optimizing black-box problems with small evaluation budgets. The BO pipeline itself is hig…
Computing Star Discrepancies with Numerical Black-Box Optimization Algorithms
François Clément, Diederick Vermetten, Jacob de Nobel +3
The star discrepancy is a measure for the regularity of a finite set of points taken from . Low discrepancy point sets are highly relevant for Quasi-Monte Car…
MA-BBOB: Many-Affine Combinations of BBOB Functions for Evaluating AutoML Approaches in Noiseless Numerical Black-Box Optimization Contexts
Diederick Vermetten, Furong Ye, Thomas Bäck +1
Extending a recent suggestion to generate new instances for numerical black-box optimization benchmarking by interpolating pairs of the well-established BBOB functions from the COm…