Quality diversity (QD) is a growing branch of stochastic optimization research that studies the
problem of generating an archive of solutions that maximize a given objective function but …

Nature, with its numerous surprising rules, serves as a rich source of creativity for the
development of artificial intelligence, inspiring researchers to create several nature-inspired …

Feature-based algorithm selection aims to automatically find the best one from a portfolio of
optimization algorithms on an unseen problem based on its landscape features. Feature …

The paper discusses derivative-free optimization (DFO), which involves minimizing a
function without access to gradients or directional derivatives, only function evaluations …

We consider solving a class of unconstrained optimization problems in which only stochastic
estimates of the objective functions are available. Existing stochastic optimization methods …

The class of algorithms called Hessian Estimation Evolution Strategies (HE-ESs) update the
covariance matrix of their sampling distribution by directly estimating the curvature of the …

When solving optimization problems with black-box approaches, the algorithms gather
valuable information about the problem instance during the optimization process. This …

Dynamic algorithm selection can be beneficial for solving numerical black-box problems, in
which we implement an online switching mechanism between optimization algorithms. In …

Abstract Derivative-Free Optimization (DFO) problems naturally arise in various domains,
from engineering design to hyperparameter optimization and beyond. This thesis introduces …

In the context of metaheuristic search algorithms, two recent approaches have been
proposed to measure algorithm similarity. The first one is based on shared search strategies …