cs-estimation-of-distribution-algorithms

Estimation of Distribution Algorithms implemented in C#

Install

Install-Package cs-estimation-of-distribution-algorithms -Version 1.0.1

Features

The current library support optimization problems in which solutions are either binary-coded or continuous vectors. The algorithms implemented for estimation-of-distribution are listed below:

PBIL
CGA (Compact Genetic Algorithm)
BOA (Bayesian Optimization Algorithm)
UMDA (Univariate Marginal Distribution Algorithm)
Cross Entropy Method
MIMIC

Usage

Solving Continuous Optimization

Running PBIL

The sample codes below shows how to solve the "Rosenbrock Saddle" continuous optmization problem using PBIL:

CostFunction_RosenbrockSaddle f = new CostFunction_RosenbrockSaddle();
            
int popSize = 8000;
PBIL s = new PBIL(popSize, f);

s.SolutionUpdated += (best_solution, step) =>
{
	Console.WriteLine("Step {0}: Fitness = {1}", step, best_solution.Cost);
};

int max_iterations = 200;
s.Minimize(f, max_iterations);

Where the CostFunction_RosenbrockSaddle is the cost function that is defined as below:

public class CostFunction_RosenbrockSaddle : CostFunction
{
	public CostFunction_RosenbrockSaddle()
		: base(2, -2.048, 2.048) // 2 is the dimension of the continuous solution, -2.048 and 2.048 is the lower and upper bounds for the two dimensions 
	{

	}

	protected override void _CalcGradient(double[] solution, double[] grad) // compute the search gradent given the solution 
	{
		double x0 = solution[0];
		double x1 = solution[1];
		grad[0] = 400 * (x0 * x0 - x1) * x0 - 2 * (1 - x0);
		grad[1] = -200 * (x0 * x0 - x1);
	}

	// Optional: if not overriden, the default gradient esimator will be provided for gradient computation
	protected override double _Evaluate(double[] solution) // compute the cost of problem given the solution 
	{
		double x0 = solution[0];
		double x1 = solution[1];

		double cost =100 * Math.Pow(x0 * x0 - x1, 2) + Math.Pow(1 - x0, 2);
		return cost;
	}

}

Running CGA

The sample codes below shows how to solve the "Rosenbrock Saddle" continuous optmization problem using CGA:

CostFunction_RosenbrockSaddle f = new CostFunction_RosenbrockSaddle();
            
int n = 1000; // sample size for the distribution 
CGA s = new CGA(n, f);

s.SolutionUpdated += (best_solution, step) =>
{
	Console.WriteLine("Step {0}: Fitness = {1}", step, best_solution.Cost);
};

int max_iterations = 2000000;
s.Minimize(f, max_iterations);

Running UMDA

The sample codes below shows how to solve the "Rosenbrock Saddle" continuous optmization problem using UMDA:

CostFunction_RosenbrockSaddle f = new CostFunction_RosenbrockSaddle();
            
int popSize = 1000; 
int selectionSize = 100;
UMDA s = new UMDA(popSize, selectionSize, f);

s.SolutionUpdated += (best_solution, step) =>
{
	Console.WriteLine("Step {0}: Fitness = {1}", step, best_solution.Cost);
};

int max_iterations = 2000000;
s.Minimize(f, max_iterations);

Running MIMIC

The sample codes below shows how to solve the "Rosenbrock Saddle" continuous optmization problem using MIMIC:

CostFunction_RosenbrockSaddle f = new CostFunction_RosenbrockSaddle();
            
int n = 1000; // population size 
MIMIC s = new MIMIC(n, f);

s.SolutionUpdated += (best_solution, step) =>
{
	Console.WriteLine("Step {0}: Fitness = {1}", step, best_solution.Cost);
};

int max_iterations = 2000000;
s.Minimize(f, max_iterations);

Running CrossEntropyMethod

The sample codes below shows how to solve the "Rosenbrock Saddle" continuous optmization problem using CrossEntropyMethod:

CostFunction_RosenbrockSaddle f = new CostFunction_RosenbrockSaddle();
            
int sampleSize = 1000; 
int selectionSize = 100;
CrossEntropyMethod s = new CrossEntropyMethod(sampleSize, selectionSize, f);

s.SolutionUpdated += (best_solution, step) =>
{
	Console.WriteLine("Step {0}: Fitness = {1}", step, best_solution.Cost);
};

int max_iterations = 2000000;
s.Minimize(f, max_iterations);

Solving Problems with Binary-encoded Solutions

Running PBIL

The samle codes below show how to solve a canonical optimization problem that look for solutions with minimum number of 1 bits in the solution:

int popSize = 8000;
int dimension = 50;
int eliteCount = 50;
PBIL s = new PBIL(popSize, dimension, eliteCount);
s.MaxIterations = 100;

s.SolutionUpdated += (best_solution, step) =>
{
	Console.WriteLine("Step {0}: Fitness = {1}", step, best_solution.Cost);
};

s.Minimize((solution, constraints) =>
{
	// solution is binary-encoded
	double cost = 0;
	// minimize the number of 1 bits in the solution
	for(int i=0; i < solution.Length; ++i)
	{
		cost += solution[i]; 
	}
	return cost;
});

Running CGA

The samle codes below show how to solve a canonical optimization problem that look for solutions with minimum number of 1 bits in the solution:

int sampleSize = 8000;
int dimension = 50;
int sampleSelectionSize = 100;
CGA s = new CGA(sampleSize, dimension, sampleSelectionSize);
s.MaxIterations = 100;

s.SolutionUpdated += (best_solution, step) =>
{
	Console.WriteLine("Step {0}: Fitness = {1}", step, best_solution.Cost);
};

s.Minimize((solution, constraints) =>
{
	// solution is binary-encoded
	double cost = 0;
	// minimize the number of 1 bits in the solution
	for(int i=0; i < solution.Length; ++i)
	{
		cost += solution[i]; 
	}
	return cost;
});

Running UMDA

The samle codes below show how to solve a canonical optimization problem that look for solutions with minimum number of 1 bits in the solution:

int sampleSize = 8000;
int dimension = 50;
int sampleSelectionSize = 100;
UMDA s = new UMDA(sampleSize, dimension, sampleSelectionSize);
s.MaxIterations = 100;

s.SolutionUpdated += (best_solution, step) =>
{
	Console.WriteLine("Step {0}: Fitness = {1}", step, best_solution.Cost);
};

s.Minimize((solution, constraints) =>
{
	// solution is binary-encoded
	double cost = 0;
	// minimize the number of 1 bits in the solution
	for(int i=0; i < solution.Length; ++i)
	{
		cost += solution[i]; 
	}
	return cost;
});

TODO

BOA algorithm still has bugs, will need to be fixed in the future release.

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