Benchmark Functions for CEC'2013 Special Session and Competition on Niching Methods for Multimodal Function Optimization
Organizers : Xiaodong Li, Andries Engelbrecht, and Michael G. Epitropakis
Version : 1.2
Developers : Michael G. Epitropakis and Xiaodong Li
Please refer to:
- https://titan.csit.rmit.edu.au/~e46507/cec13-niching/
- https://titan.csit.rmit.edu.au/~e46507/cec13-niching/competition/
If you are using any material from the current competition please cite the following work:
X. Li, A. Engelbrecht, and M.G. Epitropakis, ``Benchmark Functions for CEC'2013 Special Session and Competition on Niching Methods for Multimodal Function Optimization'', Technical Report, Evolutionary Computation and Machine Learning Group, RMIT University, Australia, 2013.
- About:
In this folder you can find all necessary source files for the benchmark suite of the CEC'2013 Special Session and Competition on Niching Methods for Multimodal Function Optimization.
The Test suite for the competition is implemented in MATLAB, JAVA, C/C++ and python.
- Documentation:
For more information please refer to the Technical Report of the Special Session/Competition
- Installation instructions:
Please unpack the archive and extract its contents in a folder:
unzip FILENAME.zip cd FILENAME
In the FILENAME folder you will find the following folder structure:
- Directory Structure:
After unpacking the archive file, you should end up with the following structure:
./ The MAIN directory, created when unpacking | +-- matlab Source code of the benchmark functions in MATLAB | | | +- figs Figures of the benchmark functions (for validation) | | | +- data Data files for the benchmark suite | +-- c++ Source code of the benchmark functions in C/C++ | | | +- plots Figures of the benchmark function (for validation) | | | +- data Data files for the benchmark suite | +-- java Source code of the benchmark functions in JAVA | | | +- CEC2013 Project files | +-- python Source code of the benchmark functions in python (v 2) | | | +- CEC2013 Project files | | | +- data Data files for the benchmark suite +-- python3 Source code of the benchmark functions in python3 | | | +- CEC2013 Project files | | | +- data Data files for the benchmark suite +-- GECCO-all-results-2016-2019 Scripts for calculating performance measures for the three scenarios of the GECCO competition
The matlab folder contains:
+ niching_func.m |-- The source code of the benchmark functions | + demo_suite.m |-- A demonstration file for using the competition source code | + count_goptima.m |-- function for counting the number of global optima in a population | + plots.m |-- MATLAB script for reproducing 1D/2D plots for the benchmark functions | + data/ |-- Folder with data files | + figs/ |-- Folder with the 1D/2D figures of the benchmark suite | + get_fgoptima.m |-- For each function, get the global's optima fitness value | + get_no_goptima.m |-- For each function, get the amount of known global optima | + get_rho.m |-- For each function, get the rho value | + get_lb.m |-- For each function, get the lower bounds of the optimization box | + get_ub.m |-- For each function, get the upper bounds of the optimization box | + get_copy_of_goptima.m |-- For each function, get the positions of the global optima
The c++ folder contains:
+ main.cpp: |-- Examples on how to use each benchmark functions. | + cec2013.h cec2013.cpp |-- Interface and implementation of the CEC 2013 competition | + cfunction.h cfunction.cpp |-- The implementation of the benchmark function suite. | + plots.cpp |-- Source code to produce data files for plotting the benchmark functions | + plots/ |-- It includes plotting data files, and the benchmark functions' figures. | + plots/plots.m |-- MATLAB script for reproducing 1D/2D plots for the benchmark functions | + Makefile: |-- A simple Makefile for easy compilation. | + data/ |-- Folder with data files | + rand2.h rand2.c |-- A simple random number generator | + randfile |-- In Linux just a symbolic link on /dev/urandom | + sortidx.h |-- Help header file for sorting
- Compilation
The Makefile provides the following three options: make clean : Cleans all unnecessary files.
make all : Compiles the main.cpp file. To see a demonstration of all benchmark functions, execute ./a.out
make plots : Compiles the plots.cpp file. To produce the necessary data files execute ./plots. It will produce 12 data files in the plots/ folder, one for each benchmark function. To produce the figures just run the plots.m MATLAB script.
The Java folder contains all necessary files to use the competition function set. Please follow the demonstration file ExampleUsage.java on how to use the source code. To reproduce the figures from the technical report please execute the Plots.java file.
The python folder contains all necessary files to use the competition function set. Please follow the demonstration file demo.py on how to use the source code. Support for python ^2.7 versions
The python3 folder contains all necessary files to use the competition function set. Please follow the demonstration file demo.py on how to use the source code. This is the python3 version of the competition.
The folder contains R scripts for calculating the competition performance metrics of all participants on all three scenarios as described in the GECCO series competitions.
- We really want to thank Dr. Jerry Swan for his fruitful comments and suggestions that helped to improve, the design and the implementation of the competition in the Java programming language.
- We also thank Dr. Daniel Molina Cabrera, for finding and correcting a bug in the Java source code.
- We also thank Dr. Catalin Stoean, for finding and correcting a bug in the source code.
- We also thank Dr. Mike Preuss, for his valuable help and contributions on the competition.
- We really want to thank Eduardo Nobre Luis, and Fabricio Olivetti de Franca for providing us a first implementation of the competition in python. This version help us to redesign and re-implement the required infrastructure in python.
- We also thank Francesco Fracchia, for finding and correcting the fitness value accuracy for shubert function
- Many thanks to Ali Ahrari, for finding and correcting a bug in the matlab implementation
- Many thanks to Simon Wessing, for finding and correcting a bug in the python implementation
- Many thanks to André L. Maravilha, for finding a bug in the MATLAB implementation
Copyright 2013, Michael G. Epitropakis, Xiaodong Li, and Andries Engelbrecht. All rights reserved.
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