private void SetFitnessFunction(FitnessFunctionDelegate argDelegate)
{
//uncheck
miXXplusYY.IsChecked = false;
miTripod.IsChecked = false;
miAlpine.IsChecked = false;
miGriewank.IsChecked = false;
miRosenbrock.IsChecked = false;
//set
ffd = argDelegate;
//preview
cvPage.Children.Clear();
if (Preview)
{
Optimizer = new PreviewDisplayer
{
LowerParamBounds = new ArrayList {
-100, -100
},
UpperParamBounds = new ArrayList {
100, 100
},
FitnessFunction = ffd
};
Optimizer.GenerationCreated += this.ShowAntibodies;
Optimizer.Optimize();
}
}
/// <summary>
/// Constructor for class
/// </summary>
/// <param name="swingObject">Specific swing GameObject the class is attached to</param>
/// <param name="entity">Specific instance of an IEntity which serves as the swinger's brain</param>
/// <param name="fitnessFunction">Fitness Function to use when evaluating current state</param>
public void Construct(GameObject swingObject, IEntity entity, FitnessFunctionDelegate fitnessFunction)
{
swing = swingObject;
swingElements = swing.transform.Find("Swing").gameObject;
robotElements = swing.transform.Find("Robot").gameObject;
swingAI = entity;
fitnessFunctionToUse = fitnessFunction;
}
/// <summary>
/// Initializes the fields of a particle and calculates the fitness of the position vector.
/// </summary>
/// <param name="ffd">Function used for the fitness calculation.</param>
/// <param name="p">The new parameter tuple.</param>
public Particle(FitnessFunctionDelegate ffd, ArrayList pos, EventHandlerDelegate fitnessEvaluated) : base(ffd, pos, fitnessEvaluated)
{
// Initial velocity is 0.0 in each dimension.
Velocity = new ArrayList();
for (int i = 0; i < Position.Count; i++)
{
Velocity.Add(0.0);
}
BestFitness = Double.MaxValue;
}
/// <summary>
/// Initializes the fields of an element and calculates the fitness of the position vector.
/// </summary>
/// <param name="fitnessFunction">Function used for the affinity calculation.</param>
/// <param name="parameters">The new parameter tuple.</param>
public BaseElement(FitnessFunctionDelegate fitnessFunction, ArrayList parameters, EventHandlerDelegate fitnessEvaluated)
{
Position = new ArrayList();
foreach (var d in parameters)
{
Position.Add(d);
}
FitnessFunction = fitnessFunction;
FitnessEvaluated += fitnessEvaluated;
Update();
}
/// <summary>
/// Method to retrieve the fitness function from it's string name
/// </summary>
/// <param name="fitnessFuncToUse">String name of the desired fitness function. Must be contained in fitnessFunctionNames.</param>
/// <returns>FitnessFunction, the method chosen from the input string.</returns>
public FitnessFunctionDelegate GetfitnessFunction(string fitnessFuncToUse)
{
//Easy method of allocating which function to use
switch (fitnessFuncToUse)
{
case "Example":
findFitness = ExampleFitnessFunction;
break;
case "StoredEnergy":
findFitness = EnergyConservation;
break;
default:
findFitness = EnergyConservation;
break;
}
return(findFitness);
}
/// <param name="fitnessFunction">Have to thread save since it will be used in several threads at the same time</param>
public StartingInfo(FitnessFunctionDelegate <Creature> fitnessFunction, Creature foreFather)
{
FitnessFunctionFactory = new DefaultFitnessFunctionFactory <Creature>(fitnessFunction);
this.foreFather = foreFather;
}
public void SetFitnessFunction(FitnessFunctionDelegate <Creature> newFitnessFunction) => SetFitnessFunctinonFactory(new DefaultFitnessFunctionFactory <Creature>(newFitnessFunction));
public MainWindow()
{
InitializeComponent();
openParams = 0;
// Lower and upper bounds for the parameters.
lbp = new ArrayList {
-100.0, -100.0
};
ubp = new ArrayList {
100.0, 100.0
};
// Initial values of the parameters to be optimized.
InitialParameters = new ArrayList {
90.0, 90.0
};
// Define whether the seeked values should be restricted to integers (true) or not (false).
Integer = new bool[] { false, false };
//Create optimizer object.
// Number of antibodies.
NA = 50;
method = 0;
Slow = false;
Preview = false;
uniqueTest = false;
// Stopping
stoppingType = StoppingType.EvaluationNumber;
ng = 100;
nev = 50000;
Ftr = 0.00001;
// Fitness function
ffd = FitnessFunctionParabola;
//clonal generation params
beta = 0.5;
ro = 0.5;
d = NA / 5;
cgn = NA / 3;
cglssr = 0.005;
cgln = 5;
// Firework params
m = 50;
a = 0.04;
b = 0.8;
Amax = 40;
mhat = NA;
// Particle swarm params
c0 = 0.8;
cp = 0.2;
cg = 0.2;
// Genetic algorithm params
P = NA / 2;
pm = 0.6;
crossoverCount = NA;
// Bee algorithm params
ExBeeCount = 10;
MaxStep = 5;
// Bacterial algorithm params
Infections = 10;
ClonesCount = 25;
// Differential Evolution params
weighf = 1.8;
crossf = 0.9;
// Haromony Search params
consid_rate = 0.95;
adjust_rate = 0.7;
range = 0.05;
}
/// <summary>
/// Method responsible for the optimization.
/// </summary>
/// <returns>An object containing the optimized values of the parameters as well as other characteristics of the optimization process.</returns>
/// <summary>
/// Returns the particle created using the initial parameters and the fitness function.
/// </summary>
/// <param name="fitnessFunction"></param>
/// <param name="parameters"></param>
/// <returns></returns>
protected override IElement GetNewElement(FitnessFunctionDelegate fitnessFunction, ArrayList parameters)
{
return(new Particle(fitnessFunction, parameters, FitnessEvaluated));
}
/// <summary>
/// Returns the element created using the initial parameters and the fitness function.
/// </summary>
/// <param name="fitnessFunction"></param>
/// <param name="parameters"></param>
/// <returns></returns>
protected virtual IElement GetNewElement(FitnessFunctionDelegate fitnessFunction, ArrayList parameters)
{
return(new BaseElement(fitnessFunction, parameters, FitnessEvaluated));
}
public DefaultFitnessFunctionFactory(FitnessFunctionDelegate <Creature> fitnessFunction)
{
provider = () => fitnessFunction;
}
