Extending Instance Space Analysis to Algorithm Configuration Spaces

Anthony Rasulo, Kate Smith-Miles, Mario A. Muñoz, Julia Handl, Manuel López-Ibáñez

Abstract: This paper describes an approach for deriving visual insights about the joint relationship between algorithm performance, algorithm parameters, and problem instance features. This involves the combined analysis and exploration of a 2D instance space, to which instances from some problem space are projected, and a 2D configuration space, to which (algorithm) configurations are projected. Extending on the dimensionality reduction problem solved in Instance Space Analysis, we define an optimisation problem for finding projections to these two spaces, with an interpretable relationship between them. Then, we describe the tools developed for probing those spaces in an investigation of the question: What characterises the algorithm configurations that perform best on a selected group of instances (or vice versa)? We demonstrate the use of these tools on synthetic data with known ground truth.