Introduction:

This project is a first of my new one-day-build mini-projects serie. It is my take on implementing a simple version of the Genetic Algorithm using C++, with a demo use case consisting of fitting a gaussian distribution to a data histogram.

General Code Description:

This code has been developed and tested on a machine running ubuntu 17.04, but should work on any linux distribution with gcc instaled.

Getting the code:

This code is available on GitHub at:
https://github.com/remizaidan/GeneticAlgorithm

To download from the command-line:

git clone https://github.com/remizaidan/GeneticAlgorithm

Dependencies:

ROOT Data Analysis Framework (tested with version 6.10). Documentation and download are avaiable here:
https://root.cern.ch

Doxygen documentation:

https://remizaidan.github.io/GeneticAlgorithm

Code structure:

The code is structured into the following sub-directories:

include Contains all header files.
src Contains all source files that are to be compiled into static libraries.
utils Contains all source files that are to be compiled into executables.
doc Contains documentation files.

After comiling, the following directories may be created:

bin Contains all executables.
lib Contains project libraries (shared or static).
.objs Contains static libraries corresponding to source files in the src folder.
.deps Contains dependency files.

Compiling and Running the demo

Compile the demo (replace <N> with the number of cores on your machine):

make -j<N>

Run the demo:

./bin/runGA.exe [options]

For the list of available options see runGA::parseCommandLine or run:

./bin/runGA.exe -h

Other compiling options:

Compile into a shared library:

make -j<N> shared

Compile into a static library:

make -j<N> static

Generate local doxygen documentation:

make doc