Large-scale Testing Global Optimization Methods with Black-box Adversarial Attacks
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Computer Science > Machine Learning
Title:Large-scale Testing Global Optimization Methods with Black-box Adversarial Attacks
Abstract:Existing global optimization benchmark suites are of a moderate size and are based on a small number of analytical functions that date back even to the 1970s. This causes a risk of biasing the development of global optimization methods. We argue that the tasks related to the black-box adversarial attack (BBAA) can serve as valuable global optimization benchmark in many-dimensional space. We demonstrate the efficiency of several types of evolutionary algorithms and other metaheuristics in solving example BBAA problems. Thus, we take a step towards convergence of global optimization methods to the challenges and needs that arise in the modern machine learning field.
| Comments: | Accepted to PPSN 2026 |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2608.13296 [cs.LG] |
| (or arXiv:2608.13296v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2608.13296
arXiv-issued DOI via DataCite (pending registration)
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