5 papers
Similarity-based Portfolio Construction for Black-box Optimization
Catalin-Viorel Dinu, Diederick Vermetten, Carola Doerr
In black-box optimization, a central question is which algorithm to use to solve a given, previously unseen, problem. Selecting a single algorithm, however, entails inherent risks:…
How Sequential Algorithm Portfolios can benefit Black Box Optimization
Catalin-Viorel Dinu, Diederick Vermetten, Carola Doerr
In typical black-box optimization applications, the available computational budget is often allocated to a single algorithm, typically chosen based on user preference with limited…
An Adaptive Re-evaluation Method for Evolution Strategy under Additive Noise
Catalin-Viorel Dinu, Yash J. Patel, Xavier Bonet-Monroig +1
The Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES) is one of the most advanced algorithms in numerical black-box optimization. For noisy objective functions, several a…
A Standardized Benchmark Set of Clustering Problem Instances for Comparing Black-Box Optimizers
Diederick Vermetten, Catalin-Viorel Dinu, Marcus Gallagher
One key challenge in optimization is the selection of a suitable set of benchmark problems. A common goal is to find functions which are representative of a class of real-world opt…
Reinforcement learning for Quantum Tiq-Taq-Toe
Catalin-Viorel Dinu, Thomas Moerland
Quantum Tiq-Taq-Toe is a well-known benchmark and playground for both quantum computing and machine learning. Despite its popularity, no reinforcement learning (RL) methods have be…