Anytime Performance Assessment in Blackbox Optimization Benchmarking
Abstract
We present concepts and recipes for the anytime performance assessment when benchmarking optimization algorithms in a blackbox scenario. We consider runtime-oftentimes measured in 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 multiobjective 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.
Domains
Computer Science [cs] Neural and Evolutionary Computing [cs.NE] Computer Science [cs] Computational Engineering, Finance, and Science [cs.CE] Computer Science [cs] Machine Learning [cs.LG] Computer Science [cs] Mathematical Software [cs.MS] Computer Science [cs] Numerical Analysis [cs.NA] Computer Science [cs] Performance [cs.PF]
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