2 citations · 2 across the 3 of their papers we have counts for
4 papers
NomadBBO: A direct-search framework for constrained mixed-variable blackbox optimization in Python
Edward Hallé-Hannan, Christophe Tribes
Mixed-variable blackbox optimization arises in simulation-based engineering and machine learning, where objective and constraint functions are expensive to evaluate and variables m…
Surrogate-based categorical neighborhoods for mixed-variable blackbox optimization
Charles Audet, Youssef Diouane, Edward Hallé-Hannan +2
In simulation-based engineering, design choices are often obtained following the optimization of complex blackbox models. These models frequently involve mixed-variable domains wit…
CatMADS: Mesh Adaptive Direct Search for constrained blackbox optimization with categorical variables
Charles Audet, Youssef Diouane, Edward Hallé-Hannan +2
Solving optimization problems in which functions are blackboxes and variables involve different types poses significant theoretical and algorithmic challenges. Nevertheless, such s…
A distance for mixed-variable and hierarchical domains with meta variables
Edward Hallé-Hannan, Charles Audet, Youssef Diouane +2
Heterogeneous datasets emerge in various machine learning and optimization applications that feature different input sources, types or formats. Most models or methods do not native…