Introduction

This is a Python wrapping using the C++ Implementation of the test suite for the Special Session on Large Scale Global Optimization at 2019 IEEE Congress on Evolutionary Computation http://cec2019.org/programs/competitions.html#cec-06.

http://www.ntu.edu.sg/home/epnsugan/index_files/CEC2019/CEC2019.htm

Note

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

K. V. Price, N. H. Awad, M. Z. Ali, P. N. Suganthan, "Problem Definitions and Evaluation Criteria for the 100-Digit Challenge Special Session and Competition on Single Objective Numerical Optimization," Technical Report, Nanyang Technological University, Singapore, November 2018.

http://web.mysites.ntu.edu.sg/epnsugan/PublicSite/Shared%20Documents/Forms/AllItems.aspx?RootFolder=%2fepnsugan%2fPublicSite%2fShared%20Documents%2fCEC%2d2019&FolderCTID=&View=%7bDAF31868%2d97D8%2d4779%2dAE49%2d9CEC4DC3F310%7d

Requirements

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

Instalation

It is pending to submit to pip, when it is ready.

Very easy, pip install cec2019comp100digit ;-).

You can also download from https://github.com/dmolina/cec2019comp100digit, and do python setup.py 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 input_data must be available in the working directory. In the python version, these files are included in the packages, so it is not needed.

Quickstart

The package is very simple to use. There is a package cec2019comp100digit with three functions:

init(fun_id, Dim) Init the function for the dimension selected.
eval(sol) Eval the solution, when sol is a numpy (or array) of dimension Dim.
end() Free resources.

Init function

>>> from cec2019comp100digit import cec2019comp100digit
>>> bench = cec2019comp100digit
>>> bench.init(3, 10) # Init function 3

Create a random solution

>>> import numpy as np
>>> sol = np.random.rand(10)

Evaluate a solution

>>> bench.eval(sol)
18010038.104525752

Freeing resources

>>> bench.end()

Contact

Python package: Daniel Molina @ Computer Science Deparment, University of Granada Please feel free to contact me at <dmolina@decsai.ugr.es> for any enquiries or suggestions.

Last Updated

C++ version <2018-12-08>
Python wrapping <2018-12-08>