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Knowledge of search-landscape features of BlackBox Optimization (BBO)problems offers valuable information in light of the Algorithm Selection and/orConfiguration problems. Exploratory Landscape Analysis (ELA) models have gainedsuccess in identifying predefined human-derived features and in facilitatingportfolio selectors to address those challenges. Unlike ELA approaches, thecurrent study proposes to transform the identification problem into an imagerecognition problem, with a potential to detect conception-free, machine-drivenlandscape features. To this end, we introduce the notion of Landscape Images,which enables us to generate imagery instances per a benchmark function, andthen target the classification challenge over a diverse generalized dataset offunctions. We address it as a supervised multi-class image recognition problemand apply basic artificial neural network models to solve it. The efficacy ofour approach is numerically validated on the noise free BBOB and IOHprofilerbenchmarking suites. This evident successful learning is another step towardautomated feature extraction and local structure deduction of BBO problems. Byusing this definition of landscape images, and by capitalizing on existingcapabilities of image recognition algorithms, we foresee the construction of anImageNet-like library of functions for training generalized detectors that relyon machine-driven features.

Toward an ImageNet Library of Functions for Global Optimization Benchmarking