Summary of A Review Of 315 Benchmark and Test Functions For Machine Learning Optimization Algorithms and Metaheuristics with Mathematical and Visual Descriptions, by M.z. Naser et al.
A Review of 315 Benchmark and Test Functions for Machine Learning Optimization Algorithms and Metaheuristics with Mathematical and Visual Descriptions
by M.Z. Naser, Mohammad Khaled al-Bashiti, Arash Teymori Gharah Tapeh, Armin Dadras Eslamlou, Ahmed Naser, Venkatesh Kodur, Rami Hawileeh, Jamal Abdalla, Nima Khodadadi, Amir H. Gandomi
First submitted to arxiv on: 13 Jun 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 This paper presents a comprehensive survey of over 300 benchmark functions used to evaluate optimization and metaheuristics algorithms. The authors catalog these functions based on their characteristics, complexity, properties, visuals, and domain implications, providing a wide view that aids in selecting appropriate benchmarks for various algorithmic challenges. The review also lists the top 25 most commonly used benchmark functions in the open literature and proposes two new, highly dimensional, dynamic, and challenging functions for testing new algorithms. By identifying gaps in current benchmarking practices, this paper suggests directions for future research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps to solve a big problem in computer science by providing a list of over 300 special test problems that are used to evaluate how well different algorithm ideas work. The authors organize these test problems into categories and provide visual descriptions, making it easier to choose the right one for a specific task. They also highlight the most commonly used test problems and suggest two new ones that could be helpful in testing new algorithms. Overall, this paper aims to help researchers and developers create better algorithm ideas by providing more information about what works well and what doesn’t. |
Keywords
* Artificial intelligence * Optimization
