Large-scale Testing Global Optimization Methods with Black-box Adversarial Attacks

• URL: http://arxiv.org/abs/2608.13296v1
• Date: Thu, 13 Aug 2026 14:26:45 GMT
• Title: Large-scale Testing Global Optimization Methods with Black-box Adversarial Attacks
• Abstract summary: We argue that the tasks related to the black-box adversarial attack (BBAA) can serve as valuable global optimization benchmark in many-dimensional space.<n>We demonstrate the efficiency of several types of evolutionary algorithms and other metaheuristics in solving example BBAA problems.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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