一种灵活通用的可解释景观分析方法与 pyXla 工具箱
A Flexible and Generic Approach for Explainable Landscape Analysis and the pyXla Toolbox
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中文总结 AI 辅助
本文提出可解释景观分析（XLA）原则性方法及 Python 包 pyXla，通用适用于多种问题类型，并展示其在多样景观特征问题上的可解释输出。
中文摘要 AI 辅助
景观分析已成功应用于理解复杂优化问题、洞察算法行为以及自动化算法选择与配置。尽管在过去几十年中开发了许多景观分析技术，但研究人员和实践者仍然难以决定哪些方法适用以及如何在实践中实现它们。现有一些工具，但它们要么局限于特定问题领域（例如，无约束的黑箱连续优化），要么在建模和测量方面能力有限。此外，景观分析的输出往往不易解释，尤其是当计算出的景观特征与实践者熟悉的问题方面不对应时。在本文中，我们提出了一种可解释景观分析（XLA）的原则性方法，并附带一个名为 pyXla 的 Python 包。该方法具有通用性，适用于具有不同表示（连续或组合）、单目标或多目标、有约束或无约束的问题。XLA 框架提供的分析程度取决于可用的数据，随着用户提供更多附加信息，分析会更为丰富。我们在具有多样景观特征的手工设计问题选择上展示了 pyXla 生成的可解释输出。
英文摘要
Landscape analysis has been successfully applied to understand complex optimisation problems, gain insights into algorithm behaviour, and automate algorithm selection and configuration. Although many landscape analysis techniques have been developed over the last decades, it remains difficult for researchers and practitioners to decide which approaches are appropriate and to implement them in practice. Some tools are available, but these are either restricted to particular problem domains (e.g., unconstrained black-box continuous optimisation), or are limited in what they model and measure. In addition, output from landscape analysis is often not easily interpretable, especially when computed landscape features do not correspond with aspects of problems that practitioners are familiar with. In this paper, we introduce a principled approach for explainable landscape analysis (XLA) with an associated Python package called pyXla. The approach is generic in that it applies to problems with different representations (continuous or combinatorial), with single or multiple objectives, with or without constraints. The extent of analysis provided by the XLA framework depends on the data available, with richer analysis offered as additional information is provided by the user. We demonstrate the explainable output produced by pyXla on a selection of hand-crafted problems with diverse landscape characteristics.
发表机构
- University of Pretoria（比勒陀利亚大学）
- African Institute for Data Science and Artificial Intelligence (AfriDSAI), University of Pretoria（非洲数据科学与人工智能研究所（AfriDSAI），比勒陀利亚大学）
- Univ. Littoral Côte d’Opale（海岸北方大学）
- University of South Africa（南非大学）
机构由 AI 辅助整理，请以论文原文为准。
