COCO: Performance Assessment

Nikolaus Hansen (1) , Anne Auger (1) , Dimo Brockhoff (2) , Dejan Tusar (2, 1) , Tea Tušar (2)

1 TAO - Machine Learning and Optimisation
2 DOLPHIN - Parallel Cooperative Multi-criteria Optimization

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.

Domaines: Réseau de neurones [cs.NE]; Apprentissage [cs.LG]; Analyse numérique [cs.NA]; Logiciel mathématique [cs.MS]; Performance et fiabilité [cs.PF]

Nikolaus Hansen, Anne Auger, Dimo Brockhoff, Dejan Tusar, Tea Tušar. COCO: Performance Assessment. 2016. ⟨hal-01315318⟩