Class MyClass

examples/so-bind/example_bind.cpp:39–143

typedef EA::GenerationType<MySolution,MyMiddleCost> Generation_Type;

class MyClass
{
public:
	std::ofstream output_file;

	MyClass(std::string output_location)
	{
		output_file.open(output_location);
		output_file<<"step"<<"\t"<<"x_best"<<"\t"<<"y_best"<<"\t"<<"cost_avg"<<"\t"<<"cost_best"<<"\n";
	}

	~MyClass()
	{
		output_file.close();
	}

	void init_genes(MySolution& p,const std::function<double(void)> &rnd01)
	{
		p.x=20.0*rnd01()-10.0;
		p.y=20.0*rnd01()-10.0;
	}

	bool eval_solution(
		const MySolution& p,
		MyMiddleCost &c)
	{
		double x=p.x;
		double y=p.y;
		// see the surface plot at:
		// https://academo.org/demos/3d-surface-plotter/?expression=x*x%2By*y%2B30.0*sin(x*100.0*sin(y)%2By*100.0*cos(x))%2B125%2B45.0*sqrt(x%2By)*sin((15.0*(x%2By))%2F(x*x%2By*y))&xRange=-10%2C%2B10&yRange=-10%2C%2B10&resolution=100
		// 
		// the middle comupations of cost:
		if(x+y>0)
		{
			double predictable_noise=30.0*sin(x*100.0*sin(y)+y*100.0*cos(x));
			c.cost_distance2=x*x+y*y+predictable_noise;
			c.cost_sqsin=125+45.0*sqrt(x+y)*sin((15.0*(x+y))/(x*x+y*y));
			return true; // genes are accepted
		}
		else
			return false; // genes are rejected
	}

	MySolution mutate(
		const MySolution& X_base,
		const std::function<double(void)> &rnd01,
		double shrink_scale)
	{
		MySolution X_new;
		bool in_range_x,in_range_y;
		const double mu=0.2*shrink_scale; // mutation radius
		do{
			X_new=X_base;
			X_new.x+=mu*(rnd01()-rnd01());
			X_new.y+=mu*(rnd01()-rnd01());
			in_range_x= (X_new.x>=-10.0 && X_new.x<10.0);
			in_range_y= (X_new.y>=-10.0 && X_new.y<10.0);
		} while(!in_range_x || !in_range_y);