GECCO Workshop on Real-Parameter Black-Box Optimization Benchmarking (BBOB 2019)

Welcome to the web page of the 9th GECCO Workshop on Real-Parameter Black-Box Optimization Benchmarking (BBOB 2019) taking place during GECCO 2019 in Prague.


held as part of the

2019 Genetic and Evolutionary Computation Conference (GECCO-2019)
July 13–17, Prague, Czech Republic
Submission Deadline: extended to Wednesday, April 10, 2019 (was: April 3, 2019)

register for news COCO quick start (scroll down a bit) latest COCO release

Quantifying and comparing the performance of optimization algorithms is a difficult and tedious task. Yet, it is highly crucial in order to recommend algorithms performing well in practice. The Black-box Optimization Benchmarking workshop series aims at bringing together researchers from the optimization field to discuss the latest achievements in (blackbox) optimization benchmarking as well as at gathering and sharing data of extensive numerical benchmarking results.

Open to all topics around blackbox optimization benchmarking, a substantial portion of the workshops’ past success can be attributed to the Comparing Continuous Optimization benchmarking platform (COCO, ) that builds the basis for all BBOB workshops and that allows algorithms to be benchmarked and performance data to be visualized effortlessly. Up to now, the BBOB workshops have covered benchmarking of blackbox optimization algorithms for single- and bi-objective, unconstrained problems in exact and noisy, as well as expensive and non-expensive scenarios.

Celebrating the tenth year anniversary of the first BBOB workshop this year, we plan a few extensions of COCO for 2019, in particular in terms of new test suites:

  • A large-scale test suite will provide the classical 24 BBOB functions in dimensions up to 640.
  • A mixed integer (single-objective) test suite will allow to test algorithms on versions of the classical BBOB test functions with some of the variables discretized.
  • A bi-objective mixed integer test suite which is a discretized version of the previously introduced bbob-biobj suite.

Like for the previous editions of the workshop, we will provide source code in various languages (C/C++, Matlab/Octave, Java, and Python) to benchmark algorithms on the various COCO test suites (besides the above, also the previously introduced single-objective suites with and without noise as well as a noiseless bi-objective suite). Postprocessing data and comparing algorithm performance will be equally automatized with COCO (up to already prepared LaTeX templates for writing papers).

Analyzing the vast amount of available benchmarking data (from 200+ experiments collected throughout the years) will be again a special focus of BBOB-2019. As always, we encourage contributions on all kinds of benchmarking aspects, in particular:

  • benchmarking expensive/Bayesian/surrogate-assisted optimization
  • comparisons between deterministic and stochastic approaches
  • benchmarking of multiobjective optimization algorithms
  • analysis of existing benchmarking data
  • the suggestion and analysis of new test functions

Interested participants of the workshop are invited to submit a paper which might or might not use the provided LaTeX templates to visualize the performance of unconstrained single- or multiobjective black-box optimization algorithms of their choice on any of the provided test suites. We encourage particularly submissions about algorithms from outside the evolutionary computation community as well as any papers related to topics around optimization algorithm benchmarking.

If participants wish to contribute to the BBOB workshop by submitting data sets, obtained with COCO, the tasks are as usual: run your favorite single- or multiobjective black-box optimizer (old or new) by using the wrappers provided (in C/C++, Python, Java, and Matlab/Octave) and run the post-processing procedure (provided as well) that will generate automatically all the material for a workshop paper (ACM compliant LaTeX templates available). A description of the algorithm and the discussion of the results completes the paper writing.

Note again that any other submission, related to black-box optimization benchmarking will be welcome as well. The submission section below gives a few examples of subjects of interest.

During the workshop, algorithms, results, and discussions will be presented by the participants. An overall analysis and comparison of all submitted algorithm data is going to be accomplished by the organizers and the overall process will be critically reviewed.

Updates and News

Get updated about the latest news regarding the workshop and releases and bugfixes of the supporting NumBBO/COCO platform, by registering at

Supporting material

The basis of the workshop is the Comparing Continuous Optimizer platform (, now rewritten fully in ANSI C with other languages calling the C code. Languages currently available are C, Java, MATLAB/Octave, and Python.

Most likely, you want to read the COCO quick start (scroll down a bit). This page also provides the code for the benchmark functions [1], for running the experiments in C, Java, Matlab, Octave, and Python, and for postprocessing the experiment data into plots, tables, html pages, and publisher-conform PDFs via provided LaTeX templates. Please refer to for more details on the general experimental set-up for black-box optimization benchmarking.

The latest (hopefully) stable release of the COCO software can be downloaded as a whole here. Please use at least version v2.2.2 for running your benchmarking experiments in 2019.

Documentation of the functions used in the bbob-biobj suite is provided at .

[1]Note that the current release of the new COCO platform does not contain the original noisy BBOB testbed yet, such that you must use the old code at for the time being if you want to compare your algorithm on the noisy testbed.


We encourage any submission that is concerned with black-box optimization benchmarking of continuous optimizers, for example papers that:

  • describe and benchmark new or not-so-new algorithms on one of the above testbeds,
  • compare new or existing algorithms from the COCO/BBOB database [2],
  • analyze the data obtained in previous editions of BBOB [2], or
  • discuss, compare, and improve upon any benchmarking methodology for continuous optimizers such as design of experiments, performance measures, presentation methods, benchmarking frameworks, test functions, ...

Paper submissions are expected to be done through the official GECCO submission system at until the (extended) deadline on April 10, 2019. ACM-compliant LaTeX templates are available in the github repository under code-postprocessing/latex-templates/.

In order to finalize your submission, we kindly ask you to fill in addition the form at where you are supposed to provide a link to your data as well if this applies. To upload your data to the web, you might want to use which offers uploads of data sets up to 50GB in size or any other provider of online data storage. Please let us know briefly in the mandatory Data field, why you do not provide any data for example in case you submit a paper unrelated to the above BBOB test suites.

[2](1, 2) The data of previously compared algorithms can be found at and is easily accessible from the python cocopp.archives module.

Important Dates

  • 2019-02-27 paper submission system opens
  • 2019-03-15 release 2.3 of the COCO platform with the new large-scale and mixed integer suites: (originally planned on 2019-03-06)
  • 2019-04-10 paper and data submission deadline (not extendable, was: April 3)
  • 2019-04-17 decision notification
  • 2019-04-24 deadline camera-ready papers
  • 2019-04-24 deadline author registration
  • 2019-07-13 or 2019-07-14 workshop

All dates are given in ISO 8601 format (yyyy-mm-dd).


  • Anne Auger, Inria Saclay - Ile-de-France, France
  • Dimo Brockhoff, Inria Saclay - Ile-de-France, France
  • Nikolaus Hansen, Inria Saclay - Ile-de-France, France
  • Tea Tušar, Jožef Stefan Institute, Ljubljana, Slovenia
  • Konstantinos Varelas, Thales LAS France SAS - Limours and Inria Saclay - Ile-de-France, France