Solver Tuning and Model Configuration
| Type of publication: | Inproceedings |
| Citation: | |
| Booktitle: | Proceedings of the 41st German Conference on Artificial Intelligence (KI 2018) |
| Series: | Lecture Notes in Artificial Intelligence |
| Volume: | 1117 |
| Year: | 2018 |
| Pages: | 141 -- 154 |
| Publisher: | Springer, Cham |
| Location: | Berlin |
| ISBN: | 978-3-030-00110-0 |
| URL: | https://link.springer.com/chap... |
| DOI: | https://doi.org/10.1007/978-3-030-00111-7_13 |
| Abstract: | This paper addresses the problem of tuning parameters of mathematical solvers to increase their performance. We investigate how solvers can be tuned for models that undergo two types of configuration: variable configuration and constraint configuration. For each type, we investigate search algorithms for data generation that emphasizes exploration or exploitation. We show the difficulties for solver tuning in constraint configuration and how data generation methods affects a training sets learning potential. |
| Keywords: | Evolutionary algorithm, machine learning, Mathematical solvers, Novelty search, Tuning mathematical solvers |
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| Added by: | [] |
| Total mark: | 0 |
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