Oct 5, 2026J/K move · Enter open · S save
Tony Ombaso, Anna S. Bosman, Arnaud Liefooghe, Katherine M. Malan+1
Department of Computer Science, University of Pretoria, Pretoria, South Africa · African Institute for Data Science and Artificial Intelligence (AfriDSAI), University of Pretoria, Pretoria, South Africa · Univ. Littoral Cˆote d’Opale, UR 4491, LISIC, F-62100 Calais, France+1
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.