Beyond Numerical Features: CNN-Driven Algorithm Selection via Contour Plots for Continuous Black-Box Optimization

TL;DR

This paper presents a novel image-based approach using CNNs to select algorithms for black-box optimization by analyzing contour plots, outperforming traditional feature-based methods.

Contribution

It introduces a contour-map visualization technique combined with CNNs for per-instance algorithm selection, offering a competitive alternative to numerical descriptors.

Findings

CNN-based selectors outperform single best solver on BBOB 2009

Image-based approach is competitive with feature-based baselines

Simple vision models can exploit landscape spatial structure for selection

Abstract

The present paper introduces a new representation-driven approach to per-instance algorithm selection, applied to black-box optimization, for automatically choosing the most promising solver from a fixed portfolio. Prior work in continuous optimization largely relies on numerical descriptors, including Exploratory Landscape Analysis features and learned embeddings such as Deep-ELA. This work studies a complementary representation: contour-map visualizations of probed landscapes. A CNN regressor takes multiple instance-specific contour views (stacked or encoded per view and aggregated) and predicts per-solver performance, enabling selection by the predicted best value. On the standard BBOB 2009 single-objective protocol, the resulting selectors significantly outperform the single best solver (SBS) and are competitive with feature-based baselines. A subsequent bi-objective evaluation under the DeepELA setting further indicates that the same image-based principle can be competitive when using windowed contour views. Overall, the results suggest that simple vision models can exploit spatial structure in probed landscapes for algorithm selection without handcrafted ELA features.