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Package for benchmark for the Real Optimization session on IEEE Congress on Evolutionary Computation CEC'2005

Project description

This is a Python wrapping using the C++ Implementation of the test suite for the Special Session on Large Scale Global Optimization at 2005 IEEE Congress on Evolutionary Computation.


If you are to use any part of this code, please cite the following publications:

    1. Suganthan, N. Hansen, J. J. Liang, K. Deb, Y.-P. Chen, A. Auger and S. Tiwari, “Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization”, Technical Report, Nanyang Technological University, Singapore, May 2005 AND KanGAL Report #2005005, IIT Kanpur, India.


  • GNU Make

  • GNU G++

  • Python

  • Cython

Testing Environment

  • Debian GNU/Linux jessie/sid

  • GNU Make 3.81

  • g++ (Debian 4.7.3-4) 4.7.3

  • Python 2.7 and Python 3.2

  • numpy 1.8.1

  • cython 0.20.1

Results with Travis-CI


Very easy, pip install cec2005real ;-).

You can also download from, and do python install [–user]. (the option –user is for installing the package locally, as a normal user (interesting when you want to run the experiments in a cluster/server without administration permissions).

To compile the source code in C++

The source code in C++ is also available. If you want to compile only the C++ version type in ‘make’ in the root directory of source code.

There are two equivalents demo executables: demo and demo2.

REMEMBER: To run the C++ version the directory cdatafiles must be available in the working directory. In the python version, these files are included in the packages, so it is not needed.


The source code has tests to check the information about each function, and the results obtained with the C version using the solution np.zeros(10) (a solution of zeros).


The package is very simple to use. There is a class Function with two functions:

  • Give information for each function: their optimum, their dimensionality, the domain search, and the expected threshold to achieve the optima.

  • Give a fitness function to evaluate solutions. It expect that these solutions are numpy arrays (vectors) but it can also work with normal arrays.

These two functionalities are done with two methods in Benchmark class:

  • get_num_functions()

    Return the number of functions in the benchmarks (15)

  • get_info()

    Return an array with the following information, where /function_id/ is the identifier of the function, a int value between 1 and 15.

    • lower, upper

      lower and upper boundaries of the domain search.

    • best

      Optimum to achieve, it is always zero, thus it can be ignored.

    • threshold

      Threshold to obtain, it is always zero, thus it can also be ignored.

    • dimension

      Dimension for the function, it is always 1000.

    It can be noticed that several data are the same for all functions. It is made for maintaining the same interface to other cec20xx competitions.

  • get_eval_function()

    It returns the fitness function to evaluate the solutions.

Examples of use

Obtain information about one function

>>> from cec2005real.cec2005 import Function
>>> fbench = Function(1, 10)
>>> fbench.get_info()
{'best': 0.0,
 'dimension': 1000,
 'lower': -100.0,
 'threshold': 0,
 'upper': 100.0}

Evaluate a solution

>>> fun_fitness = fbench.get_eval_function()
>>> fun_fitness(sol)


Python package

Daniel Molina @ Computer Science Deparment, University of Cadiz Please feel free to contact me at <> for any enquiries or suggestions.

Last Updated:

  • C++ version 2005

  • Python wrapping <2015-10-30>

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