Summary of On Constructing Algorithm Portfolios in Algorithm Selection For Computationally Expensive Black-box Optimization in the Fixed-budget Setting, by Takushi Yoshikawa and Ryoji Tanabe
On Constructing Algorithm Portfolios in Algorithm Selection for Computationally Expensive Black-box Optimization in the Fixed-budget Setting
by Takushi Yoshikawa, Ryoji Tanabe
First submitted to arxiv on: 13 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 The proposed algorithm selection system for offline optimization problems selects the most promising optimizer from a pre-defined portfolio of efficient optimizers, complementary to each other. This system has shown effectiveness in various optimization problems, including black-box optimization. The paper highlights the importance of considering the number of function evaluations used in the sampling phase when constructing algorithm portfolios, which is often ignored in previous studies. The results demonstrate that the proposed approach performs significantly better than traditional methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary An important new system helps choose the best way to solve complex problems. This system picks the most effective method from a group of good ones. It’s very useful for solving big problems where we don’t know exactly what will work best. The paper talks about why it’s important to consider how much effort is used when trying out different methods. If we do this, our results get better. |
Keywords
» Artificial intelligence » Optimization
