Visualization of evolutionary runs with isometric mapping

The visualization of evolutionary blackbox optimization runs is important to understand evolutionary processes that may require the interaction with or intervention by the practitioner. But high-dimensional processes are not easy to visualize. In this work, we introduce an approach based on isometric mapping (ISOMAP) that maps continuous evolutionary runs in high-dimensional decision spaces to low-dimensional latent spaces that can be visualized. The embeddings are post-processed by computing a convex hull of embeddings, interpolating contour plots, and finally tracking the evolutionary run by marking the best solutions of each generation. Example plots demonstrate the capabilities of the approach. Experiments with the co-ranking matrix measure show that ISOMAP performs equally or better locally linear embedding and principal component analysis in maintaining neighborhoods of high-dimensional solutions.