Beyond Numerical Features: CNN-Driven Algorithm Selection via Contour Plots for Continuous Black-Box Optimization
Two new research papers explore novel methods for selecting the best algorithm for continuous black-box optimization tasks. One paper, GeoPAS, uses geometric probing to create 2D slices of the objective landscape, encoding these slices to represent problem instances and then selecting solvers based on a composite score. The other paper, using CNNs, visualizes probed landscapes as contour plots, feeding these images into a convolutional neural network to predict solver performance and guide selection. Both approaches aim to significantly outperform relying on a single best solver, demonstrating improved efficiency and robustness across various optimization scenarios. AI
IMPACT These novel approaches to algorithm selection could lead to more efficient and robust optimization processes in various scientific and engineering fields.