This code reimplements the original Comparing Continous Optimizer platform (http://coco.gforge.inria.fr/), now rewritten fully in ANSI C with the other languages calling the C code. Languages currently available are C, Java, MATLAB, and Python. Languages available in near future are C++ and Octave. Contributions to link further languages are more than welcome.

1. For a machine running experiments

• A C compiler, such as gcc
• make, such as GNU make
• Python >=2.6 with setuptools installed

2. For a machine running the post-processing

• Python 2.6 or 2.7 with numpy and matplotlib installed. We recommend to install the Anaconda library. Please note that Python 3 is not yet supported with the post-processing part of NumBBO/CoCO!

Under Windows, two alternative compile toolchains can be used:

1. Cygwin which comes with gcc and make, available in 32- and 64-bit versions.
2. MinGW's gcc (http://www.mingw.org/) and GNU make (http://gnuwin32.sourceforge.net/packages/make.htm). MinGW only comes in 32-bit, but also runs on 64-bit machines.

For using git under Windows (optional), we recommend installing TortoiseGit.

Additional requirements for running an algorithm in a specific language.

• Java: none, but see here for details on the compilation
• Python: none, see here for details on the installation
• MATLAB: at least MATLAB 2008, for details, see here

We tested the framework on Mac OSX, Ubuntu linux, Fedora linux, and Windows (XP, 7, 10) in various combinations of 32-bit and 64-bit compilers, python versions etc. Naturally, we cannot guarantee that the framework runs on any combination of operating system and software installed. In case you experience some incompatibilies, we will be happy if you can document them in detail on our issue tracker.

Download the COCO framework code from github by clicking here, CAVEAT: this code is still under development, and unzip the zip file.

1. In a system shell, cd into the numbbo (framework root) folder, where the file do.py lies. Type, i.e. execute, one of the following commands once

  python do.py run-c
  python do.py run-java
  python do.py run-matlab
  python do.py run-python

depending which language is used to run the experiments. run-* will build the respective code and run the example experiment once. The build result and the example experiment code can be found under code-experiments/build/*.

3. If the example experiment runs, connect your favorite algorithm to Coco: copy the code-experiments/build/YOUR-FAVORITE-LANGUAGE folder to another location. Replace the call to the random search optimizer in the example experiment file by a call to your algorithm (the details vary), see respective the read-me's and example experiment files:

• C read me and example experiment
• Java read me and example experiment
• Matlab read me and example experiment
• Python read me and example experiment`

Another entry point for your own experiments can be the code-experiments/examples folder. In any case, update the output (observer options) result_folder and the algorithm_name and info in the experiment file.

4. Now you can run your favorite algorithm on the bbob-biobj (for multi-objective algorithms) or on the bbob suite (for single-objective algorithms). Output is automatically generated in the specified result folder.

5. Postprocess your data from the results folder by typing

   python code-postprocessing/bbob_pproc/rungeneric.py YOURRESULTFOLDER
   

A folder named ppdata by default will be generated (the folder name can be changed by the -o FOLDERNAME option). Note that you can also compare more than one algorithm by specifying more algorithm result folders, separated by blanks.

Within the postprocessing's output folder, you will find pdfs of all kinds of plots (e.g. data profiles). For the single-objective bbob suite, they can be used to produce a summary pdf via LaTeX. The corresponding templates in ACM format can be found in the code-postprocessing/latex-templates folder. LaTeX templates for the multi-objective bbob-biobj suite will follow in a later release. A basic html output is also available in the result folder of the postprocessing (file templateBBOBarticle.html).

6. Once your algorithm runs well, increase the budget in your experiment script, if necessary implement randomized independent restarts, and follow the above steps successively until you are happy.

If you detect bugs or other issues, please let us know by opening an issue in our issue tracker at https://github.com/numbbo/coco/issues.

• the do.py file in the root folder is a tool to build the entire distribution. do.py is a neat and simplifying replacement for make. It has switches for just building some languages etc, e.g.

  python ./do.py build-c python ./do.py build-java python ./do.py build-matlab python ./do.py build-python python ./do.py build # builds all

are valid commands code-experiments/build/LANGUAGE/.

• the code-experiments/build folder is to a large extend the output folder of the ./do.py build command.

  • the exampleexperiment.??? files in the build folder are the entry points to understand the usage of the code (as end-user). They are supposed to actually be executable (in case, after compilation, which should be taken care of by do.py and/or make) and run typically random search on (some of) the provided benchmark suites.

• documentation and examples might not be too meaningful for the time being, even though code-experiments/documentation/onion.py describes a (heavily) used design pattern (namely: inheritance) in a comparatively understandable way (though the implementation in C naturally looks somewhat different). In the future, documentation will be contained mainly in the docs/ subfolder with the source code extracted automatically into pdfs in this folder and to web pages under the numbbo.github.io/ domain.

• the code-experiments/src folder is where most of the important/interesting things happen. Many files provide comparatively decent documentation at the moment which are translated via doxygen into a more readable web page at numbbo.github.io/coco-doc/C/. Generally:

  • coco.h is the public interface, in particular as used in the demo.c file, however check out https://code.google.com/p/numbbo/issues/detail?id=98
  • coco_internal.h provides the type definition of coco_problem_t
  • coco_suite.c is code that deals with an entire benchmark suite (i.e. a set of functions, eg. sweeping through them etc...)
  • coco_generics.c is somewhat generic code, e.g. defining a function call via coco_evaluate_function etc
  • coco_problem.c is the implementation of the coco_problem_t type/object (allocation etc).
  • observer / logger files implement data logging (as wrappers around a coco problem inheriting thereby all properties of a coco problem)
  • most other files implement more or less what they say, e.g. the actual benchmark functions, transformations, benchmark suites, etc.
  • currently, three benchmark suites and corresponding logging facilities are implemented:
    • bbob: standard single-objective BBOB benchmark suite with 24 noiseless, scalable test functions
    • bbob-biobj: a bi-objective benchmark suite, combining 10 selected functions from the bbob suite, resulting in 55 noiseless functions
    • toy: a simple, probably easier-to-understand example for reading and testing

• code-experiments/tools are a few meta-tools, mainly the amalgamate.py to merge all the C code into one file

• code-experiments/test contains unit- and integration-tests, mainly for internal use

• code-postprocessing/bbob_pproc contains the postprocessing code, written in python, with which algorithm data sets can be read in and the performance of the algorithms can be displayed in terms of data profiles, ERT vs. dimension plots, or simple tables.

• code-postprocessing/latex-templates contains LaTeX templates for displaying algorithm performances in publisher-conform PDFs for the GECCO and CEC conferences (for the single-objective bbob suite only, templates for the bi-objective bbob-biobj suite will be provided in a later release).

• docs should contain an updated version of the documentation, see above.

• howtos contains a few text files with internal howtos.

If you see something like this

PYTHON  setup.py install --user in code-experiments/build/python
ERROR: return value=1
Traceback (most recent call last):
 File "setup.py", line 8, in <module>
   import setuptools
ImportError: No module named setuptools

Traceback (most recent call last):
 File "do.py", line 562, in <module>
   main(sys.argv[1:])
 File "do.py", line 539, in main
   elif cmd == 'build-python': build_python()
 File "do.py", line 203, in build_python
   python('code-experiments/build/python', ['setup.py', 'install', '--user'])
 File "/vol2/twagner/numbbo/code-experiments/tools/cocoutils.py", line 92, in p                                         ython
   universal_newlines=True)
 File "/usr/local/lib/python2.7/subprocess.py", line 575, in check_output
   raise CalledProcessError(retcode, cmd, output=output)
subprocess.CalledProcessError: Command '['/usr/local/bin/python', 'setup.py', 'i                                        nstall', '--user']' returned non-zero exit status 1

setuptools needs to be installed:

    pip install setuptools

or easy_install setuptools should do the job.

At the moment, we experience some problems with the installation of the python module of Coco under Ubuntu linux (see numbbo#317). We are working on a fix.

Also the Matlab wrapper does not always work under linux with the current code: an issue is filed for the Ubuntu operating system at numbbo#318

With the more complex SMS-EMOA example, also an issue is known under Mac (numbbo#308). The problem is related to the compilation of the external C++ hypervolume calculation in hv.cpp. This issue is less critical as the example experiment runs under all Mac OSX machines with Matlab, that we tried.

• The C code features an object oriented implementation, where the coco_problem_t is the most central data structure / object. coco.h, example_experiment.c and coco_internal.h are probably the best pointers to start to investigate the code (but see also below). coco_problem_t defines a benchmark function instance (in a given dimension), and is called via coco_evaluate_function.

• Building, running, and testing of the code is done by merging/amalgamation of all C-code into a single C file, coco.c, and coco.h. (by calling do.py, see above). Like this it becomes very simple to include/use the code in different projects.

• Cython is used to compile the C interface in build/python/interface.pyx. The Python module installation file setup.py uses the compiled interface.c, if interface.pyx has not changed.

• We continuously test the code through the open source automation server Jenkins on one ubuntu 12.04 machine, one OSX 10.9 machine, and one 32-bit Windows 7 machine with cygwin.

• Downloading this repository
  • via the above "Download ZIP" button or
  • by typing git clone https://github.com/numbbo/coco.git or
  • via https://github.com/numbbo/coco/archive/master.zip in your browser
• The BBOB workshop series, which uses the NumBBO/Coco framework extensively, can be tracked at here
• Stay informed about the BBOB workshop series and releases of the NumBBO/Coco software by registering at http://coco.gforge.inria.fr/register
• More detailed documentation of the existing benchmark suites can be found here:
  • for the "BBOB" testbed at http://coco.lri.fr/downloads/download15.03/bbobdocfunctions.pdf with the experimental setup at http://coco.lri.fr/downloads/download15.03/bbobdocexperiment.pdf
  • for the bbob-biobj functions at http://numbbo.github.io/bbob-biobj-functions-doc
• Online documentation of the NumBBO/Coco API (i.e. for the ANSI C code) is available at http://numbbo.github.io/coco-doc/C