gealib

A fast parallel genetic algorithm implementation in C++.

Code example

#include <vector>
#include "gealib.h"

using namespace std;
using namespace gealib;

# define PI 3.14159265358979323846

// This is the fitness function we want to maximize
ftype myfunction(vector<ftype>& v)
{
	ftype x = v[0];
	ftype y = v[1];
	ftype z = v[2];
	
	return  (ftype) (sin(1.3 * x*PI + 1.33 * cos(10 * y*PI + 0.01 * cos(0.22* z*PI))) + sin(z * 2) + 0.01*(x + y + z));
}

int main()
{
	// create an instance of the ga class 
	// specifying the fitness function
	ga ga(myfunction);

	// Define the parameters by name, min and max value, required accuracy
	parameter x("x", -1.0, 1.0, 0.000001);
	parameter y("y", -1.0, 1.0, 0.000001);
	parameter z("z", -1.0, 1.0, 0.000001);

	// Add parameters to the genetic algorithm
	ga.add_parameter(x);
	ga.add_parameter(y);
	ga.add_parameter(z);

	// run 500 iterations, show progress every 50 iterations
	ga.run(500, 50);

	getchar();
	return 0;
}

Expected output

The above code is expected to produce the following output:

Iteration    50,  optimal fitness value     2.022464
Iteration   100,  optimal fitness value     2.023973
Iteration   150,  optimal fitness value     2.023984
Iteration   200,  optimal fitness value     2.023985
Iteration   250,  optimal fitness value     2.023985
Iteration   300,  optimal fitness value     2.023985
Iteration   350,  optimal fitness value     2.023985
Iteration   400,  optimal fitness value     2.023985
Iteration   450,  optimal fitness value     2.023985
Iteration   500,  optimal fitness value     2.023985


-------------------------------
| Parameter | Parameter value |
-------------------------------
|        x= |        0.709295 |
|        y= |        0.902860 |
|        z= |        0.787904 |
-------------------------------

Genetic Algorithm parameters

The above example runs with the default parameters. The following parameters can be specified with set methods.

Mutation Rate

Specifies the probability of chromosome mutation

ga.set_mutation_rate(0.03);

Crossover Rate

Specifies the percentage of the population that participates in crossover

ga.set_crossover_rate(0.5);

Elite Rate

Specifies the percentage of the population that participates in next generation without crossover.

ga.set_elite_rate(0.1);

Population Size

Specifies the size of the chromosome population.

ga.set_population_size(1024);

Fitness Function

Specifies the fitness function to be used

ga.set_fitness_function(function);

Crossover Method

Specifies the crossover method to be used. Should be an instance of a class inheriting from gealib::crossover.

ga.set_crossover(crossover);

Mutation Method

Specifies the mutation method to be used. Should be an instance of a class inheriting from gealib::mutation.

ga.set_mutation(mutation);