Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP
Automated Algorithm Configuration (AAC) usually takes a global perspective: it identifies a parameter configuration for an (optimization) algorithm that maximizes a performance metric over a set of instances. However, the optimal choice of parameters strongly depends on the instance at hand and shou...
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| Vydáno v: | IEEE Symposium on Computational Intelligence in Multi-Criteria Decision Making s. 361 - 368 |
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| Jazyk: | angličtina |
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IEEE
05.12.2023
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| ISSN: | 2472-8322 |
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| Abstract | Automated Algorithm Configuration (AAC) usually takes a global perspective: it identifies a parameter configuration for an (optimization) algorithm that maximizes a performance metric over a set of instances. However, the optimal choice of parameters strongly depends on the instance at hand and should thus be calculated on a per-instance basis. We explore the potential of Per-Instance Algorithm Configuration (PIAC) by using Reinforcement Learning (RL). To this end, we propose a novel PIAC approach that is based on deep neural networks. We apply it to predict configurations for the Lin-Kernighan heuristic (LKH) for the Traveling Salesperson Problem (TSP) individually for every single instance. To train our PIAC approach, we create a large set of 100 000 TSP instances with 2 000 nodes each - currently the largest benchmark set to the best of our knowledge. We compare our approach to the state-of-the-art AAC method Sequential Model-based Algorithm Configuration (SMAC). The results show that our PIAC approach outperforms this baseline on both the newly created instance set and established instance sets. |
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| AbstractList | Automated Algorithm Configuration (AAC) usually takes a global perspective: it identifies a parameter configuration for an (optimization) algorithm that maximizes a performance metric over a set of instances. However, the optimal choice of parameters strongly depends on the instance at hand and should thus be calculated on a per-instance basis. We explore the potential of Per-Instance Algorithm Configuration (PIAC) by using Reinforcement Learning (RL). To this end, we propose a novel PIAC approach that is based on deep neural networks. We apply it to predict configurations for the Lin-Kernighan heuristic (LKH) for the Traveling Salesperson Problem (TSP) individually for every single instance. To train our PIAC approach, we create a large set of 100 000 TSP instances with 2 000 nodes each - currently the largest benchmark set to the best of our knowledge. We compare our approach to the state-of-the-art AAC method Sequential Model-based Algorithm Configuration (SMAC). The results show that our PIAC approach outperforms this baseline on both the newly created instance set and established instance sets. |
| Author | Trautmann, Heike Heins, Jonathan Bossek, Jakob Ludger Preub, Oliver Vinzent Seiler, Moritz Rook, Jeroen |
| Author_xml | – sequence: 1 givenname: Moritz surname: Vinzent Seiler fullname: Vinzent Seiler, Moritz email: moritz.seiler@uni-muenster.de organization: University of Münster,Data Science: Statistics and Optimization,Münster,Germany – sequence: 2 givenname: Jeroen surname: Rook fullname: Rook, Jeroen email: j.g.rook@utwente.nl organization: University of Twente,Data Management and Biometrics,Enschede,Netherlands – sequence: 3 givenname: Jonathan surname: Heins fullname: Heins, Jonathan email: jonathan.heins@tu-dresden.de organization: TU Dresden,Big Data Analytics in Transportation,Dresden,Germany – sequence: 4 givenname: Oliver surname: Ludger Preub fullname: Ludger Preub, Oliver email: oliver.preuss@uni-muenster.de organization: University of Münster,Data Science: Statistics and Optimization,Münster,Germany – sequence: 5 givenname: Jakob surname: Bossek fullname: Bossek, Jakob email: bossek@aim.rwth-aachen.de organization: Aachen University,Chair for AI Methodology RWTH,Aachen,Germany – sequence: 6 givenname: Heike surname: Trautmann fullname: Trautmann, Heike email: trautmann@wi.uni-muenster.de organization: University of Münster,Data Science: Statistics and Optimization,Münster,Germany |
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| SubjectTerms | Artificial neural networks Benchmark testing Computational modeling Deep Reinforcement Learning Measurement Optimization Per-Instance Algorithm Configuration Prediction algorithms Reinforcement learning Traveling Salesperson Problem |
| Title | Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP |
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