Abstract
Empirical data plays an important role in evolutionary computation research. To make better use of the available data, ontologies have been proposed in the literature to organize their storage in a structured way. However, the full potential of these formal methods to capture our domain knowledge has yet to be demonstrated. In this work, we evaluate a performance prediction model built on top of the extension of the recently proposed OPTION ontology. More specifically, we first extend the OPTION ontology with the vocabulary needed to represent modular black-box optimization algorithms. Then, we use the extended OPTION ontology, to create knowledge graphs with fixed-budget performance data for two modular algorithm frameworks, modCMA, and modDE, for the 24 noiseless BBOB benchmark functions. We build the performance prediction model using a knowledge graph embedding-based methodology. Using a number of different evaluation scenarios, we show that a triple classification approach, a fairly standard predictive modeling task in the context of knowledge graphs, can correctly predict whether a given algorithm instance will be able to achieve a certain target precision for a given problem instance. This approach requires feature representation of algorithms and problems. While the latter is already well developed, we hope that our work will motivate the community to collaborate on appropriate algorithm representations.
Abstract (translated)
经验数据在进化计算研究中扮演了重要作用。为了更好地利用现有的数据,文献中提出了基于结构的本体论,以组织其存储。然而,这些正式方法 capturing 我们领域的知识的潜力仍未得到充分证明。在这项工作中,我们评估了建立在最近提出的选项本体论扩展基础上的性能预测模型。具体而言,我们首先扩展了选项本体论,以代表模块化黑盒优化算法的模块级表示。然后,我们使用扩展的选项本体论,为两个模块算法框架 modCMA 和 modDE 创建固定预算性能数据的知识图,并计算了 24 无噪声BBOB基准函数的精度。我们使用知识图嵌入方法构建性能预测模型。通过多种不同的评估场景,我们表明,基于三元分类的方法,在知识图的背景下,是一种相当标准的预测建模任务,可以正确预测给定算法实例是否能够为给定问题实例实现某种目标精度。这种方法需要算法和问题的特征表示。虽然后者已经相当成熟,我们希望我们的工作能够激励社区合作制定适当的算法表示。
URL
https://arxiv.org/abs/2301.09876
