Anytime Performance Assessment in Blackbox Optimization Benchmarking

We present concepts and recipes for the anytime performance assessment when benchmarking optimization algorithms in a blackbox scenario. We consider runtime-oftentimes measured in the number of blackbox evaluations needed to reach a target quality-to be a universally measurable cost for solving a problem. Starting from the graph that depicts the solution quality versus runtime, we argue that runtime is the only performance measure with a generic, meaningful, and quantitative interpretation. Hence, our assessment is solely based on runtime measurements. We discuss proper choices for solution quality indicators in single- and multi-objective optimization, as well as in the presence of noise and constraints. We also discuss the choice of the target values, budget-based targets, and the aggregation of runtimes by using simulated restarts, averages, and empirical cumulative distributions which generalize convergence graphs of single runs. The presented performance assessment is to a large extent implemented in the comparing continuous optimizers (COCO) platform freely available at https://github.com/numbbo/coco.

Automated summary

This paper presents concepts and recipes for the anytime performance assessment of benchmarking optimization algorithms in a blackbox scenario. The authors argue that runtime is the only performance measure with a generic, meaningful, and quantitative interpretation, and thus their assessment is solely based on runtime measurements. They discuss proper choices for solution quality indicators, target values, and the aggregation of runtimes.