Although deep reinforcement learning methods can learn effective policies for challenging problems, the underlying algorithms are complex, and training times are often long. This study investigates how several state-of-the-art versions of Evolution Strategies perform compared to…
Classical space-filling designs often fail to provide reliable statistical results for Exploratory Landscape Analysis (ELA) when only limited evaluation budgets are available, as commonly occurs in high-dimensional problems or other resource-constrained settings, resulting in noi…
Designing effective multi-objective Bayesian optimization (MOBO) algorithms requires balancing many interdependent design choices whose optimal configuration is problem-dependent and typically demands deep expertise. We extend the LLaMEA framework to MOBO, using large language mo…