2016/05/11 by Nikolaus Hansen, Anne Auger, Hansen, Nikolaus +7
Computer Science · Engineering · #Distributed and Parallel Computing Systems #FOS: Computer and information sciences #Neural and Evolutionary Computing (cs.NE) #Numerical Methods and Algorithms #Parallel Computing and Optimization Techniques #Reservoir Engineering and Simulation Methods
paper · pdf · doi:10.48550/arxiv.1605.03560
openalex publication_date 2016/05/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We present an any-time performance assessment for benchmarking numerical optimization algorithms in a black-box scenario, applied within the COCO benchmarking platform. The performance assessment is based on runtimes measured in number of objective function evaluations to reach one or several quality indicator target values. We argue that runtime is the only available measure with a generic, meaningful, and quantitative interpretation. We discuss the choice of the target values, runlength-based targets, and the aggregation of results by using simulated restarts, averages, and empirical distribution functions.