基于黑盒对抗攻击的大规模测试全局优化方法
Large-scale Testing Global Optimization Methods with Black-box Adversarial Attacks
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中文总结 AI 辅助
该研究提出黑盒对抗攻击（BBAA）相关任务可作为高维全局优化基准，通过实验验证多种进化算法与元启发式算法解决BBAA问题的效率，助力全局优化方法适配现代机器学习需求。
中文摘要 AI 辅助
现有全局优化基准套件规模中等，基于少量可追溯至20世纪70年代的解析函数，这存在使全局优化方法开发产生偏差的风险。我们认为，黑盒对抗攻击（BBAA）相关任务可作为高维空间中极具价值的全局优化基准。我们展示了多种进化算法及其他元启发式算法在解决示例BBAA问题时的效率，从而为全局优化方法向现代机器学习领域出现的挑战与需求靠拢迈出了一步。
英文摘要
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.
发表机构
- Warsaw University of Technology（华沙理工大学）
- Institute of Computer Science（计算机科学研究所）
机构由 AI 辅助整理，请以论文原文为准。
