一个可扩展的测试问题生成器,用于顺序转移优化
IEEE transactions on cybernetics
|March 20, 2025
概括
针对顺序转移优化 (STO) 问题的新基准套件解决了现有方法的局限性. 它引入了相似性分布和可扩展的生成器,用于各种任务关系,使得算法评估更可靠.
科学领域:
- 机器学习 机器学习
- 优化算法 优化算法
- 人工智能的人工智能
背景情况:
- 顺序转移优化 (STO) 正在引起人们的兴趣,但缺乏标准化的基准.
- 现有的STO测试问题通常是手动配置的,限制了可扩展性,导致了偏差的算法性能.
- 由于缺乏全面的,可扩展的评估工具,STO算法的系统比较受到阻碍.
研究的目的:
- 为系统评估序列转移优化 (STO) 算法引入一个新的基准套件.
- 解决现有的STO测试问题的局限性,包括手动配置和缺乏可扩展性.
- 通过多样化和可定制的任务关系,为揭示微妙的算法行为提供一个平台.
主要方法:
- 介绍了四个概念来描述STO问题 (STOPs).
- 定义了"相似性分布",以量化评估源和目标任务最佳解决方案之间的关系.
- 开发了一个可扩展的问题生成器,并采用了一种新的反向策略来定制相似性分布.
主要成果:
- 创建了一个12个STOP的基准套件,具有定制的相似关系.
- 新的基准揭示了诸如有偏见的可转移性表示和与搜索体验无关的性能改进等以前未被检测到的发现.
- 展示了生成器的可扩展性和捕捉多样化,现实世界的相似关系的能力.
结论:
- 开发的基准套件为评估STO算法提供了一个强大的平台.
- 新型问题生成器和相似性分布指标使得STO研究更可靠,更具普遍性.
- 这项工作有助于更深入地了解STO算法在各种任务场景中的行为和性能.
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