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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Vydané v:	IEEE Symposium on Computational Intelligence in Multi-Criteria Decision Making s. 361 - 368
Hlavní autori:	Vinzent Seiler, Moritz, Rook, Jeroen, Heins, Jonathan, Ludger Preub, Oliver, Bossek, Jakob, Trautmann, Heike
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 05.12.2023
Predmet:	Artificial neural networks Benchmark testing Computational modeling Deep Reinforcement Learning Measurement Optimization Per-Instance Algorithm Configuration Prediction algorithms Reinforcement learning Traveling Salesperson Problem
ISSN:	2472-8322
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