Robbert Reijnen

According to our database, Robbert Reijnen authored at least 7 papers between 2020 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

The first AI4TSP competition: Learning to solve stochastic routing problems.

Yingqian Zhang, Laurens Bliek, Paulo da Costa, Reza Refaei Afshar, Robbert Reijnen, Tom Catshoek, Daniël Vos, Sicco Verwer, Fynn Schmitt-Ulms, André Hottung, Tapan Shah, Meinolf Sellmann, Kevin Tierney, Carl Perreault-Lafleur, Caroline Leboeuf, Federico Bobbio, Justine Pepin, Warley Almeida Silva, Ricardo Gama, Hugo L. Fernandes, Martin Zaefferer, Manuel López-Ibáñez, Ekhine Irurozki

Artif. Intell., June, 2023

Job Shop Scheduling Benchmark: Environments and Instances for Learning and Non-learning Methods.

Robbert Reijnen, Kjell van Straaten, Zaharah Allah Bukhsh, Yingqian Zhang

CoRR, 2023

Learning to Adapt Genetic Algorithms for Multi-Objective Flexible Job Shop Scheduling Problems.

Robbert Reijnen, Yingqian Zhang, Zaharah Allah Bukhsh, Mateusz Guzek

Proceedings of the Companion Proceedings of the Conference on Genetic and Evolutionary Computation, 2023

2022

Learning Adaptive Evolutionary Computation for Solving Multi-Objective Optimization Problems.

Remco Coppens, Robbert Reijnen, Yingqian Zhang, Laurens Bliek, Berend Steenhuisen

CoRR, 2022

Operator Selection in Adaptive Large Neighborhood Search using Deep Reinforcement Learning.

Robbert Reijnen, Yingqian Zhang, Hoong Chuin Lau, Zaharah Allah Bukhsh

CoRR, 2022

Deep Reinforcement Learning for Adaptive Parameter Control in Differential Evolution for Multi-Objective Optimization.

Robbert Reijnen, Yingqian Zhang, Zaharah Allah Bukhsh, Mateusz Guzek

Proceedings of the IEEE Symposium Series on Computational Intelligence, 2022

2020

Combining Deep Reinforcement Learning with Search Heuristics for Solving Multi-Agent Path Finding in Segment-based Layouts.

Robbert Reijnen, Yingqian Zhang, Wim Nuijten, Çaglar Senaras, Mariana Goldak-Altgassen

Proceedings of the 2020 IEEE Symposium Series on Computational Intelligence, 2020