🤖 AI Summary
This study addresses the challenge of automatically selecting the best solver for each instance in continuous black-box optimization. It proposes a novel image-based algorithm selection approach that, for the first time, uses contour plots of objective functions as input to convolutional neural networks (CNNs), enabling direct learning of spatial landscape structures from visual representations without relying on handcrafted numerical features such as those from Exploratory Landscape Analysis (ELA). The method employs multi-view stacked or encoded contour images and is trained and evaluated within the BBOB benchmark suite and the DeepELA bi-objective evaluation framework. Experimental results demonstrate that the proposed approach significantly outperforms the single best solver on the BBOB 2009 single-objective benchmark and achieves performance comparable to feature-based methods. Further bi-objective experiments confirm its competitiveness, highlighting the effectiveness and novelty of image-driven strategies in algorithm 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.