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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| Published in: | IEEE Symposium on Computational Intelligence in Multi-Criteria Decision Making pp. 361 - 368 |
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| Main Authors: | , , , , , |
| Format: | Conference Proceeding |
| Language: | English |
| Published: |
IEEE
05.12.2023
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| Subjects: | |
| ISSN: | 2472-8322 |
| Online Access: | Get full text |
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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 |
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| Snippet | Automated Algorithm Configuration (AAC) usually takes a global perspective: it identifies a parameter configuration for an (optimization) algorithm that... |
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| StartPage | 361 |
| 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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