Knowledge of search-landscape features of BlackBox Optimization (BBO)
problems offers valuable information in light of the Algorithm Selection and/or
Configuration problems. Exploratory Landscape Analysis (ELA) models have gained
success in identifying predefined human-derived features and in facilitating
portfolio selectors to address those challenges. Unlike ELA approaches, the
current study proposes to transform the identification problem into an image
recognition problem, with a potential to detect conception-free, machine-driven
landscape features. To this end, we introduce the notion of Landscape Images,
which enables us to generate imagery instances per a benchmark function, and
then target the classification challenge over a diverse generalized dataset of
functions. We address it as a supervised multi-class image recognition problem
and apply basic artificial neural network models to solve it. The efficacy of
our approach is numerically validated on the noise free BBOB and IOHprofiler
benchmarking suites. This evident successful learning is another step toward
automated feature extraction and local structure deduction of BBO problems. By
using this definition of landscape images, and by capitalizing on existing
capabilities of image recognition algorithms, we foresee the construction of an
ImageNet-like library of functions for training generalized detectors that rely
on machine-driven features.

对黑盒优化问题的领域特征进行研究，探索性景观分析（ELA）模型成功地识别了预定义的人造特征，并为解决这些挑战的组合选择器提供了帮助。本研究提出了利用图像识别方法解决这个问题的方法，并在大型数据集上应用基本的人工神经网络模型进行分类，成功实现了机器驱动的特征提取。