3 papers
cs.NE2025
Speeding Up Hyper-Heuristics With Markov-Chain Operator Selection and the Only-Worsening Acceptance Operator
Abderrahim Bendahi, Benjamin Doerr, Adrien Fradin +1
The move-acceptance hyper-heuristic was recently shown to be able to leave local optima with astonishing efficiency (Lissovoi et al., Artificial Intelligence (2023)). In this work,…
cs.NE2025
Unlearning Works Better Than You Think: Local Reinforcement-Based Selection of Auxiliary Objectives
Abderrahim Bendahi, Adrien Fradin, Matthieu Lerasle
We introduce Local Reinforcement-Based Selection of Auxiliary Objectives (LRSAO), a novel approach that selects auxiliary objectives using reinforcement learning (RL) to support th…
cs.DS2024
On Constrained and k Shortest Paths
Abderrahim Bendahi, Adrien Fradin
Finding a shortest path in a graph is one of the most classic problems in algorithmic and graph theory. While we dispose of quite efficient algorithms for this ordinary problem (li…