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在分类紧的遗传算法运行时的尾部边界.
Ryoki Hamano1, Kento Uchida2, Shinichi Shirakawa3
1CyberAgent, Inc., Tokyo, Japan hamano_ryoki_xa@cyberagent.co.jp.
Evolutionary computation
|October 1, 2024
概括
本研究为分类优化问题引入了分类紧基因算法 (ccGA). 它分析了ccGA对线性函数的运行时间复杂性,为离散优化提供了洞察力.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 优化算法 优化算法
背景情况:
- 进化算法的理论分析主要集中在二进制优化上.
- 在分类领域的黑盒优化具有重要的实际应用,但在理论上不太被探索.
- 像紧的遗传算法 (cGA) 这样的现有算法对于二进制域是有效的.
研究的目的:
- 介绍和理论分析一个基于概率模型的算法,用于分类黑子优化.
- 将紧基因算法 (cGA) 的分析扩展到分类领域.
- 调查关键参数 (类别K,维度D,学习速率η) 对算法运行时间的影响.
主要方法:
- 开发了使用分类分布的分类紧基因算法 (ccGA),样本大小为2.
- 进行了ccGA的理论运行时间分析.
- 在分类OneMax (COM) 和KVal基准函数上调查了运行时间的尾部边界.
主要成果:
- 以高概率推导出对分类OneMax (COM) 的运行时间复杂度为O (((Dln ((DK) /η).
- 以高概率推导出KVal的运行时间复杂度为 Θ(DlnK/η).
- 证明ccGA分析对二进制域的cGA分析进行了概括.
结论:
- 分类紧基因算法 (ccGA) 为分类领域的离散黑子优化提供了一种可行的方法.
- 理论运行时间分析为ccGA在问题尺寸,类别和学习率方面的表现提供了宝贵的见解.
- 这项工作将进化算法的理论理解扩展到更广泛的离散优化问题的类别.
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