# COCO: A Platform for Comparing Continuous Optimizers in a Black-Box Setting

Source: [COCO: A Platform for Comparing Continuous Optimizers in a Black-Box Setting](<https://www.sota2.com/research/paper/68fa57491e209131ce9a4183>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T14:38:35.243Z

Authors: Nikolaus Hansen, Anne Auger, Raymond Ros, Olaf Mersmann, Tea Tu\\v{s}ar, Dimo Brockhoff

Published: 2016-03-29T14:18:52.000Z

## Abstract

We introduce COCO, an open source platform for Comparing Continuous Optimizers in a black-box setting. COCO aims at automatizing the tedious and repetitive task of benchmarking numerical optimization algorithms to the greatest possible extent. The platform and the underlying methodology allow to benchmark in the same framework deterministic and stochastic solvers for both single and multiobjective optimization. We present the rationales behind the (decade-long) development of the platform as a general proposition for guidelines towards better benchmarking. We detail underlying fundamental concepts of COCO such as the definition of a problem as a function instance, the underlying idea of instances, the use of target values, and runtime defined by the number of function calls as the central performance measure. Finally, we  give a quick overview of the basic code structure and the currently available test suites.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1603.08785>)

[PDF](<https://arxiv.org/pdf/1603.08785.pdf>)