A Scalable Test Problem Generator for Sequential Transfer Optimization

IF 9.4 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Xiaoming Xue;Cuie Yang;Liang Feng;Kai Zhang;Linqi Song;Kay Chen Tan
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引用次数: 0

Abstract

Despite the increasing interest in sequential transfer optimization (STO), a comprehensive benchmark suite for systematically comparing various STO algorithms remains underexplored. Existing test problems, which are often manually configured and lack scalability, can result in biased and nongeneralizable algorithm performance. In light of the above, we first introduce four concepts for characterizing STO problems (STOPs) in this study and present an important feature, namely similarity distribution, to quantitatively delineate the relationship between the optimal solutions of source and target tasks. Subsequently, we present general design guidelines for STOPs and introduce a problem generator that demonstrates strong scalability. Specifically, the similarity distribution of a problem can be easily customized through a novel inverse generation strategy, allowing for a continuous spectrum that captures the diverse similarity relationships present in real-world scenarios. Lastly, a benchmark suite comprising 12 STOPs, characterized by a range of customized similarity relationships, has been developed using the proposed generator and will serve as a platform for examining various STO algorithms. For instance, biased transferability representation, irregular mapping learning behaviors, and performance improvements unrelated to search experience are significant empirical findings that previous benchmarks failed to reveal, yet can be effectively identified through our test problems. The source code of the proposed problem generator is available at https://github.com/XmingHsueh/STOP-G.
时序传输优化的可伸缩测试问题生成器。
尽管对顺序传输优化(STO)的兴趣越来越大,但用于系统比较各种STO算法的综合基准套件仍未得到充分开发。现有的测试问题通常是手动配置的,缺乏可伸缩性,这可能导致有偏差和不可推广的算法性能。鉴于此,我们首先在本研究中引入了表征STO问题(stop)的四个概念,并提出了一个重要特征,即相似性分布,以定量描述源任务和目标任务的最优解之间的关系。随后,我们提出了stop的一般设计准则,并介绍了一个具有强大可扩展性的问题生成器。具体来说,可以通过一种新的逆生成策略轻松地定制问题的相似度分布,从而允许捕获现实场景中存在的各种相似关系的连续谱。最后,使用提议的生成器开发了一个包含12个stop的基准套件,其特征是一系列定制的相似性关系,并将作为检查各种STO算法的平台。例如,有偏见的可转移性表示,不规则的映射学习行为,以及与搜索经验无关的性能改进是以前的基准测试未能揭示的重要经验发现,但可以通过我们的测试问题有效地识别出来。建议的问题生成器的源代码可从https://github.com/XmingHsueh/STOP-G获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IEEE Transactions on Cybernetics
IEEE Transactions on Cybernetics COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, CYBERNETICS
CiteScore
25.40
自引率
11.00%
发文量
1869
期刊介绍: The scope of the IEEE Transactions on Cybernetics includes computational approaches to the field of cybernetics. Specifically, the transactions welcomes papers on communication and control across machines or machine, human, and organizations. The scope includes such areas as computational intelligence, computer vision, neural networks, genetic algorithms, machine learning, fuzzy systems, cognitive systems, decision making, and robotics, to the extent that they contribute to the theme of cybernetics or demonstrate an application of cybernetics principles.
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