一系列基于深度学习的无冗余方法，用于表征单目标连续健身景观
A Collection of Deep Learning-based Feature-Free Approaches for Characterizing Single-Objective Continuous Fitness Landscapes
摘要：
探索性景观分析是一种强大的技术，用于数值表征单目标连续优化问题的景观。景观的见解是至关重要的问题的理解，以及评估基准集的多样性和组成。尽管这些功能具有无可辩驳的实用性，但它们也有自己的弊病和缺点。因此，在这项工作中，我们提供了一系列不同的方法来表征优化景观。与传统的景观特征类似，我们需要一个小的初始样本。然而，我们不是基于该样本计算特征，而是开发原始样本的替代表示。这些范围从点云到2D图像，因此完全没有特征。我们在BBOB测试平台上演示和验证了我们设计的方法，并在深度学习的帮助下预测了高层次的、基于专家的景观属性，如多模态程度和漏斗结构的存在。我们的方法的质量与传统的景观特征方法相当。因此，我们提供了一个令人兴奋的新的视角，利用问题的信息，如问题的理解和算法设计，以及自动化的算法配置和选择的每一个研究领域。
英文摘要：
Exploratory Landscape Analysis is a powerful technique for numerically characterizing landscapes of single-objective continuous optimization problems. Landscape insights are crucial both for problem understanding as well as for assessing benchmark set diversity and composition. Despite the irrefutable usefulness of these features, they suffer from their own ailments and downsides. Hence, in this work we provide a collection of different approaches to characterize optimization landscapes. Similar to conventional landscape features, we require a small initial sample. However, instead of computing features based on that sample, we develop alternative representations of the original sample. These range from point clouds to 2D images and, therefore, are entirely feature-free. We demonstrate and validate our devised methods on the BBOB testbed and predict, with the help of Deep Learning, the high-level, expert-based landscape properties such as the degree of multimodality and the existence of funnel structures. The quality of our approaches is on par with methods relying on the traditional landscape features. Thereby, we provide an exciting new perspective on every research area which utilizes problem information such as problem understanding and algorithm design as well as automated algorithm configuration and selection.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
