As autonomous robot and sensor technologies have advanced, utilisation of autonomous
mobile robots (AMRs) in material handling has grown quickly, owing especially to their …

This work presents a hyper-heuristic approach to online learning, which combines Monte
Carlo Tree Search with multiple local search operators selected on the fly during the search …

The present study applies Algorithm Selection (AS) to Adaptive Operator Selection (AOS) for
further improving the performance of the AOS methods. AOS aims at delivering high …

Most works on Multi-Armed Bandits focus the evaluations of their methods on a global
accuracy performance metric. In the case of Contextual Multi-Armed Bandit (CMAB), the …

Dynamic island models are population-based algorithms for solving optimization problems,
where the individuals of the population are distributed on islands. These subpopulations of …

We present a memetic algorithm with adaptive operator selection for k-coloring and
weighted vertex coloring. Our method uses online selection to adaptively determine the …

Designing portfolio adaptive selection strategies is a promising approach to gain in
generality when tackling a given optimization problem. However, we still lack much …

Dans cette thèse, nous nous intéressons à la résolution de problèmes de coloration de
graphe. Tout d'abord, nous étudions des propriétés théoriques de ces problèmes, qui …

Dans la littérature, la plupart des travaux sur les bandits manchots sont évalués à l'aide
d'une mesure de la préci-sion globale. Concernant les bandit-manchots contextuels, les …

Sélection automatique d'opérateurs dans un arbre de recherche de Monte-Carlo pour la
coloration de graphe pondéré Page 1 HAL Id: hal-04041711 https://hal.science/hal-04041711 …