3 citations · 6 across the 5 of their papers we have counts for
7 papers
Revisit the Algorithm Selection Problem for TSP with Spatial Information Enhanced Graph Neural Networks
Ya Song, Laurens Bliek, Yingqian Zhang
Algorithm selection is a well-known problem where researchers investigate how to construct useful features representing the problem instances and then apply feature-based machine l…
Digital Twin Applications in Urban Logistics: An Overview
Abdo Abouelrous, Laurens Bliek, Yingqian Zhang
Urban traffic attributed to commercial and industrial transportation is observed to largely affect living standards in cities due to external effects pertaining to pollution and co…
Learning Adaptive Evolutionary Computation for Solving Multi-Objective Optimization Problems
Remco Coppens, Robbert Reijnen, Yingqian Zhang +2
Multi-objective evolutionary algorithms (MOEAs) are widely used to solve multi-objective optimization problems. The algorithms rely on setting appropriate parameters to find good s…
Machine Learning for Combinatorial Optimisation of Partially-Specified Problems: Regret Minimisation as a Unifying Lens
Stefano Teso, Laurens Bliek, Andrea Borghesi +4
It is increasingly common to solve combinatorial optimisation problems that are partially-specified. We survey the case where the objective function or the relations between variab…
The First AI4TSP Competition: Learning to Solve Stochastic Routing Problems
Laurens Bliek, Paulo da Costa, Reza Refaei Afshar +19
This paper reports on the first international competition on AI for the traveling salesman problem (TSP) at the International Joint Conference on Artificial Intelligence 2021 (IJCA…
Continuous surrogate-based optimization algorithms are well-suited for expensive discrete problems
Rickard Karlsson, Laurens Bliek, Sicco Verwer +1
One method to solve expensive black-box optimization problems is to use a surrogate model that approximates the objective based on previous observed evaluations. The surrogate, whi…