Summary of A Survey Of Meta-features Used For Automated Selection Of Algorithms For Black-box Single-objective Continuous Optimization, by Gjorgjina Cenikj et al.
A Survey of Meta-features Used for Automated Selection of Algorithms for Black-box Single-objective Continuous Optimization
by Gjorgjina Cenikj, Ana Nikolikj, Gašper Petelin, Niki van Stein, Carola Doerr, Tome Eftimov
First submitted to arxiv on: 8 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Neural and Evolutionary Computing (cs.NE)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper surveys recent advances in algorithm selection for single-objective continuous black-box optimization. It explores how machine learning models can be used to select, configure, and predict the performance of algorithms for unseen problem instances. The authors review key contributions in this area, including representation learning of meta-features for optimization problem instances, algorithm instances, and their interactions. They identify gaps in the current state-of-the-art and suggest ideas for further development. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to choose the best algorithm for a particular problem. Researchers have been trying to figure out which algorithm works best for a specific problem instance. The paper explores ways that machine learning models can help with this task, including selecting the right algorithm, configuring it correctly, and predicting its performance. It also reviews what has already been done in this area. |
Keywords
» Artificial intelligence » Machine learning » Optimization » Representation learning
