Summary of Landscape-aware Automated Algorithm Configuration Using Multi-output Mixed Regression and Classification, by Fu Xing Long et al.
Landscape-Aware Automated Algorithm Configuration using Multi-output Mixed Regression and Classification
by Fu Xing Long, Moritz Frenzel, Peter Krause, Markus Gitterle, Thomas Bäck, Niki van Stein
First submitted to arxiv on: 2 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Neural and Evolutionary Computing (cs.NE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers investigate the use of randomly generated functions (RGF) for training predictive models in landscape-aware algorithm selection problems. They find that RGF covers a broader range of optimization problem classes compared to the traditional black-box optimization benchmarking (BBOB) suite. The authors focus on automated algorithm configuration (AAC), which involves selecting the best-suited algorithm and fine-tuning its hyperparameters based on the landscape features of problem instances. To evaluate their approach, they analyze the performance of dense neural network (NN) models in handling multi-output mixed regression and classification tasks using different training datasets, including RGF and many-affine BBOB (MA-BBOB) functions. The results show that near-optimal configurations can be identified using the proposed approach, which often outperforms default off-the-shelf configurations. Furthermore, the predicted configurations are competitive against the single best solver in many cases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this study, scientists explore a new way to train models for solving complex problems. They compare different methods and find that one method, called RGF, is better at training models for certain types of problems. The researchers also try to improve how they select the best model by fine-tuning its settings based on the problem it’s trying to solve. They test their approach using different datasets and find that it often works well and can even beat the performance of other, more experienced methods. |
Keywords
* Artificial intelligence * Classification * Fine tuning * Neural network * Optimization * Regression
