Analyzing Single-objective Black-box Optimization Algorithms Using the Empirical Attainment Function
Manuel López‐Ibáñez, Diederick Vermetten, Johann Dréo, Carola Doerr · Proceedings of the Genetic and Evolutionary Computation Conference Companion · 2025
A widely accepted way to assess the performance of iterative blackbox optimizers is to analyze their empirical cumulative distribution function (ECDF) of pre-defined quality targets achieved not later than a given runtime. In this work, we consider an alternative approach, based on the empirical attainment function (EAF) and we show that the target-based ECDF is an approximation of the EAF. We argue that the EAF has several advantages over the target-based ECDF. In particular, it does not require defining a priori quality targets per function, captures performance differences more precisely, and enables the use of additional summary statistics that enrich the analysis. We also show that the average area over the convergence curves is a simpler-to-calculate, but equivalent, measure of anytime performance. These analyses are available in the IOHanalyzer platform.