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();
}
}
public RoutingTest(IOptimizationMethod optimizer, RouteTable routeTable, Canvas cvPage, TextBox textBox)
{
this.optimizer = optimizer;
this.fullTable = routeTable;
this.cvPage = cvPage;
this.tbResults = textBox;
this.pathLines = new ArrayList();
this.cars = new List <Car>();
int pointsCount = routeTable.Points.Count;//Összes pont száma
int pathPmax = 1;
for (int i = 0; i < 9; i++)
{
int pointC = RNG.Next(pointsCount) + 1;//Aktuális utvonal pontjainak a száma
while (pointC <= 3)
{
pointC = RNG.Next(pointsCount) + 1;
}
if (pointC > pathPmax)
{
pathPmax = pointC;
}
ArrayList path = new ArrayList();
for (int j = 1; j < pointC; j++)
{
int rnd = RNG.Next(pointsCount);
while (path.Contains(rnd) || rnd == 0)
{
rnd = RNG.Next(pointsCount);
}
path.Add(rnd);
}
pathLines.Add(path);
}
for (int i = 0; i < 5; i++)
{
this.cars.Add(new Car()
{
index = i + 1, MaxPathPoint = (int)((RNG.Next(pathPmax) + 1) * 3), CurrentDailyPath = 0, MaxDailyPath = (RNG.Next(pathPmax) + 1) * 2, MaxPathDistance = (RNG.Next(pathPmax) + 1) * 150
});
Car car = this.cars[cars.Count - 1];
car.MaxPathDistanceRes = car.MaxPathDistance;
}
}
private void OptMethod(int id)
{
switch (id)
{
case 0: //Firework
Optimizer = new Firework
{
// Number of particles in the swarm.
NumberOfElements = NA,
m = m,
ymax = double.MaxValue,
a = a,
b = b,
Amax = Amax,
mhat = mhat,
StoppingType = stoppingType,
// Number of generations.
StoppingNumberOfGenerations = ng,
// Number of allowed affinity evaluations.
StoppingNumberOfEvaluations = nev,
// Affinity treshold.
StoppingFitnessTreshold = Ftr,
Slow = false
};
break;
case 1: //Particle Swarm
Optimizer = new ParticleSwarm
{
// Number of particles in the swarm.
NumberOfElements = NA,
// Number of generations.
StoppingNumberOfGenerations = ng,
c0 = c0,
// Multiplication factor for the distance to the personal best position.
cp = cp,
// Multiplication factor for the distance to the global best position.
cg = cg,
// Number of allowed fitness evaluations.
StoppingNumberOfEvaluations = nev,
// Fitness treshold.
StoppingFitnessTreshold = Ftr,
StoppingType = stoppingType,
Slow = false
};
break;
case 2: //Clonal generation
Optimizer = new ClonalGeneration
{
// Size of the antibody pool.
NumberOfElements = NA,
// Number of antibodies selected for cloning.
NumberSelectedForCloning = cgn,
// Parameter determining the number of clones created for an antibody that was selected for cloning. (0,1]
Beta = beta,
// Number of antibodies created with random parameters in each new generation.
RandomAntibodiesPerGeneration = d,
// Mutation coefficient (0,1].
Ro = ro,
// Stopping criteria.
StoppingType = stoppingType,
// Number of generations.
StoppingNumberOfGenerations = ng,
// Number of allowed affinity evaluations.
StoppingNumberOfEvaluations = nev,
// Affinity treshold.
StoppingFitnessTreshold = Ftr,
Slow = false
};
break;
case 3:
Optimizer = new GeneticAlgorithm
{
// Size of the individual pool.
NumberOfElements = NA,
// Number of parents in each generation.
ParentsInEachGeneration = P,
// The probability of mutation.
MutationProbability = pm,
// The number of crossovers in each generation.
CrossoverPerGeneration = crossoverCount,
// Number of allowed fitness evaluations.
StoppingNumberOfEvaluations = nev,
// Fitness treshold.
StoppingFitnessTreshold = Ftr,
// Number of generations.
StoppingNumberOfGenerations = ng,
// Stopping criteria.
StoppingType = stoppingType,
Slow = false
};
break;
case 4: //Clonal generation local
Optimizer = new ClonalGenerationLocal
{
LowerParamBounds = new ArrayList {
-100.0
},
UpperParamBounds = new ArrayList {
100.0
},
// Size of the antibody pool.
NumberOfElements = NA,
// Number of antibodies selected for cloning.
NumberSelectedForCloning = cgn,
// Parameter determining the number of clones created for an antibody that was selected for cloning. (0,1]
Beta = beta,
// Number of antibodies created with random parameters in each new generation.
RandomAntibodiesPerGeneration = d,
// Mutation coefficient (0,1].
Ro = ro,
// RelativeStepSize
StepSizeRelative = cglssr,
// Local search/get
LocalSearchesPerGeneration = cgln,
// Stopping criteria.
StoppingType = stoppingType,
// Number of generations.
StoppingNumberOfGenerations = ng,
// Number of allowed affinity evaluations.
StoppingNumberOfEvaluations = nev,
// Affinity treshold.
StoppingFitnessTreshold = Ftr,
Slow = false
};
break;
case 5:
Optimizer = new CoordinateDescent
{
NumberOfElements = NA,
StepSizeRelative = 0.6,
// Number of allowed fitness evaluations.
StoppingNumberOfEvaluations = nev,
// Fitness treshold.
StoppingFitnessTreshold = Ftr,
// Number of generations.
StoppingNumberOfGenerations = ng,
// Stopping criteria.
StoppingType = stoppingType,
Slow = false
};
break;
case 6:
Optimizer = new ArtificialBeeColony
{
NumberOfElements = NA,
//Felderitő méhek maximális keresési számas egy ciklus alatt
MaxStep = MaxStep,
//Felderítő méhek száma
ExBeeCount = ExBeeCount,
// Number of allowed fitness evaluations.
StoppingNumberOfEvaluations = nev,
// Fitness treshold.
StoppingFitnessTreshold = Ftr,
// Number of generations.
StoppingNumberOfGenerations = ng,
// Stopping criteria.
StoppingType = stoppingType,
Slow = false
};
break;
case 7:
Optimizer = new BacterialForaging
{
// Size of the antibody pool.
NumberOfElements = NA,
ClonesCount = ClonesCount,
Infections = Infections,
// Number of allowed fitness evaluations.
StoppingNumberOfEvaluations = nev,
// Fitness treshold.
StoppingFitnessTreshold = Ftr,
// Number of generations.
StoppingNumberOfGenerations = ng,
// Stopping criteria.
StoppingType = stoppingType,
Slow = false
};
break;
case 8:
Optimizer = new DifferentialEvolution()
{
weighf = weighf,
crossf = crossf,
NumberOfElements = NA,
// Number of allowed fitness evaluations.
StoppingNumberOfEvaluations = nev,
// Fitness treshold.
StoppingFitnessTreshold = Ftr,
// Number of generations.
StoppingNumberOfGenerations = ng,
// Stopping criteria.
StoppingType = stoppingType,
Slow = false
};
break;
case 9:
Optimizer = new SelfOrgMigrating
{
NumberOfElements = NA,
PRT = 0.6,
PathLength = 10,
PopSize = 7,
Step = 0.6,
Type = SelfOrgMigratingType.AllToAll,
// Number of allowed fitness evaluations.
StoppingNumberOfEvaluations = nev,
// Fitness treshold.
StoppingFitnessTreshold = Ftr,
// Number of generations.
StoppingNumberOfGenerations = ng,
// Stopping criteria.
StoppingType = stoppingType,
Slow = false
};
break;
case 10:
Optimizer = new HaromonySearch
{
NumberOfElements = NA,
consid_rate = consid_rate,
adjust_rate = adjust_rate,
range = range,
// Number of allowed fitness evaluations.
StoppingNumberOfEvaluations = nev,
// Fitness treshold.
StoppingFitnessTreshold = Ftr,
// Number of generations.
StoppingNumberOfGenerations = ng,
// Stopping criteria.
StoppingType = stoppingType,
Slow = false
};
break;
case 11:
Optimizer = new NelderMead
{
NumberOfElements = NA,
// Number of allowed fitness evaluations.
StoppingNumberOfEvaluations = nev,
// Fitness treshold.
StoppingFitnessTreshold = Ftr,
// Number of generations.
StoppingNumberOfGenerations = ng,
// Stopping criteria.
StoppingType = stoppingType,
Slow = false
};
break;
}
}
private async void OptimizeButton_Click(object sender, RoutedEventArgs e)
{
/*if (fuzzySystem.Type == FuzzyType.Sugeno && CheckOutput.IsChecked == true)
* {
* MessageBox.Show("Output optimization is not valid for Sugeno-type fuzzy systems.", "Warning", MessageBoxButton.OK, MessageBoxImage.Warning);
* return;
* }*/
// Disable the optimize button and the optimization parameters
OptimizeButton.IsEnabled = false;
UseButton.IsEnabled = false;
RadioBaseRatio.IsEnabled = false;
RadioRefPoint.IsEnabled = false;
CheckInput.IsEnabled = false;
CheckOutput.IsEnabled = false;
// Initialize variables
ArrayList initParams = new ArrayList();
ArrayList lbp = new ArrayList();
ArrayList ubp = new ArrayList();
// Set up initial parameters
if (CheckInput.IsChecked == true)
{
foreach (InputOutput input in fuzzySystem.Inputs)
{
foreach (MembershipFunction MF in input.MFs)
{
if (RadioRefPoint.IsChecked == true)
{
initParams.Add((double)(MF.GetReferencePoint()));
lbp.Add((double)(input.Range[0]));
ubp.Add((double)(input.Range[1]));
}
if (RadioBaseRatio.IsChecked == true)
{
initParams.Add((double)(MF.GetTrapBaseRatio()));
lbp.Add(0.0);
ubp.Add(0.95);
}
}
}
}
if (CheckOutput.IsChecked == true)
{
foreach (InputOutput output in fuzzySystem.Outputs)
{
foreach (MembershipFunction MF in output.MFs)
{
if (RadioRefPoint.IsChecked == true)
{
initParams.Add((double)(MF.GetReferencePoint()));
lbp.Add((double)(output.Range[0]));
ubp.Add((double)(output.Range[1]));
}
if (RadioBaseRatio.IsChecked == true)
{
initParams.Add((double)(MF.GetTrapBaseRatio()));
lbp.Add(0.0);
ubp.Add(0.95);
}
}
}
}
bool[] ints = new bool[initParams.Count];
// Set up the optimizer object depending on the settings
switch (MethodId)
{
case 0: //Firework
Optimizer = new Firework
{
// Number of particles in the swarm.
NumberOfElements = NA,
m = m,
ymax = double.MaxValue,
a = a,
b = b,
Amax = Amax,
mhat = mhat,
Slow = false
};
break;
case 1: //Particle Swarm
Optimizer = new ParticleSwarm
{
// Number of particles in the swarm.
NumberOfElements = NA,
c0 = c0,
// Multiplication factor for the distance to the personal best position.
cp = cp,
// Multiplication factor for the distance to the global best position.
cg = cg,
Slow = false
};
break;
case 2: //Clonal generation
if (cgn > NA)
{
MessageBox.Show("Number of antibodies selected for cloning at the end of each generation greater than the number of antibodies.", "ERROR", MessageBoxButton.OK);
OptimizeButton.IsEnabled = true;
return;
}
Optimizer = new ClonalGeneration
{
// Size of the antibody pool.
NumberOfElements = NA,
// Number of antibodies selected for cloning.
NumberSelectedForCloning = cgn,
// Parameter determining the number of clones created for an antibody that was selected for cloning. (0,1]
Beta = beta,
// Number of antibodies created with random parameters in each new generation.
RandomAntibodiesPerGeneration = d,
// Mutation coefficient (0,1].
Ro = ro,
Slow = false
};
break;
case 3:
Optimizer = new GeneticAlgorithm
{
// Size of the individual pool.
NumberOfElements = NA,
// Number of parents in each generation.
ParentsInEachGeneration = P,
// The probability of mutation.
MutationProbability = pm,
// The number of crossovers in each generation.
CrossoverPerGeneration = crossoverCount,
Slow = false
};
break;
case 4: //Clonal generation
if (cgn > NA)
{
MessageBox.Show("Number of antibodies selected for cloning at the end of each generation greater than the number of antibodies.", "ERROR", MessageBoxButton.OK);
OptimizeButton.IsEnabled = true;
return;
}
Optimizer = new ClonalGenerationLocal
{
// Size of the antibody pool.
NumberOfElements = NA,
// Number of antibodies selected for cloning.
NumberSelectedForCloning = cgn,
// Parameter determining the number of clones created for an antibody that was selected for cloning. (0,1]
Beta = beta,
// Number of antibodies created with random parameters in each new generation.
RandomAntibodiesPerGeneration = d,
// Mutation coefficient (0,1].
Ro = ro,
// Local searches per generation
LocalSearchesPerGeneration = cgln,
Slow = false
};
break;
}
// Set common parameters
Optimizer.InitialParameters = initParams;
Optimizer.LowerParamBounds = lbp;
Optimizer.UpperParamBounds = ubp;
// Bounds are required before setting this value
if (MethodId == 4)
{
((ClonalGenerationLocal)Optimizer).StepSizeRelative = cglssr;
}
Optimizer.Integer = ints;
Optimizer.FitnessFunction = FitnessFunctionMSE;
Optimizer.StoppingNumberOfGenerations = StopGenerationNumber;
Optimizer.StoppingNumberOfEvaluations = StopAffinityEvaluation;
Optimizer.StoppingFitnessTreshold = StopFitnessTreshold;
Optimizer.StoppingType = StopType;
// Output string
string outstr = "Initial parameters: " + List(initParams) + "\r\n";
// Open progress window and begin the optimization
ProgressWindow = new OptimizationProgressWindow();
ProgressWindow.OptWindow = this;
ProgressWindow.Show();
if (IoputMfWindows != null)
{
foreach (MFsPreviewWindow window in IoputMfWindows)
{
window.Close();
}
}
IoputMfWindows = new List <MFsPreviewWindow>();
foreach (InputOutput input in fuzzySystem.Inputs)
{
IoputMfWindows.Add(new MFsPreviewWindow(input));
IoputMfWindows[IoputMfWindows.Count - 1].Show();
}
foreach (InputOutput output in fuzzySystem.Outputs)
{
IoputMfWindows.Add(new MFsPreviewWindow(output));
IoputMfWindows[IoputMfWindows.Count - 1].Show();
}
Optimizer.GenerationCreated += OnNewGeneration;
// <DELAYER>
/*var z = Task.Run(() => (delayer()));
* int y = await z;*/
// </DELAYER>
var x = Task.Run(() => (Optimizer.Optimize()));
var Results = await x;
ProgressWindow.Close();
// Add results to output string
outstr += "Initial MSE: " + Results.InfoList[InfoTypes.InitialFitness] + "\r\n" +
"Final parameters: " + List(Results.OptimizedParameters) + "\r\n" +
"Final MSE: " + $"{Results.InfoList[InfoTypes.FinalFitness],10:F6}" + "\r\n" +
"Number of generations: " + Results.InfoList[InfoTypes.Generations] + "\r\n" +
"Number of fitness evaluations: " + Results.InfoList[InfoTypes.Evaluations] + "\r\n" +
"Best affinities in each generation: " + List((ArrayList)Results.InfoList[InfoTypes.Affinities]) + "\r\n" +
"Optimization time in milliseconds: " + Results.InfoList[InfoTypes.ExecutionTime];
fuzzySystem = GetModifiedFuzzySystem(Results.OptimizedParameters);
FuzzyBox.Text = fuzzySystem.ToString();
// Display results and reenable the optimize button
MessageBox.Show(outstr, "Optimization finished", MessageBoxButton.OK);
OptimizeButton.IsEnabled = true;
UseButton.IsEnabled = true;
RadioBaseRatio.IsEnabled = true;
RadioRefPoint.IsEnabled = true;
CheckInput.IsEnabled = true;
CheckOutput.IsEnabled = true;
}
private void OptMethod(int id)
{
switch (id)
{
case 0: //Firework
Optimizer = new Firework
{
InitialParameters = InitialParameters,
LowerParamBounds = LowerParamBounds,
UpperParamBounds = UpperParamBounds,
Integer = Integer,
FitnessFunction = FitnessFunction,
// Number of particles in the swarm.
NumberOfElements = NumberOfElements,
m = m,
ymax = double.MaxValue,
a = a,
b = b,
Amax = Amax,
mhat = NumberOfElements,
StoppingType = StoppingType,
// Number of generations.
StoppingNumberOfGenerations = StoppingNumberOfGenerations,
// Number of allowed affinity evaluations.
StoppingNumberOfEvaluations = StoppingNumberOfEvaluations,
// Affinity treshold.
StoppingFitnessTreshold = StoppingFitnessTreshold,
Slow = Slow
};
break;
case 1: //Particle Swarm
Optimizer = new ParticleSwarm
{
InitialParameters = InitialParameters,
LowerParamBounds = LowerParamBounds,
UpperParamBounds = UpperParamBounds,
Integer = Integer,
FitnessFunction = FitnessFunction,
// Number of particles in the swarm.
NumberOfElements = NumberOfElements,
// Number of generations.
StoppingNumberOfGenerations = StoppingNumberOfGenerations,
c0 = c0,
// Multiplication factor for the distance to the personal best position.
cp = cp,
// Multiplication factor for the distance to the global best position.
cg = cg,
// Number of allowed fitness evaluations.
StoppingNumberOfEvaluations = StoppingNumberOfEvaluations,
// Fitness treshold.
StoppingFitnessTreshold = StoppingFitnessTreshold,
StoppingType = StoppingType,
Slow = Slow
};
break;
case 2: //Clonal generation
Optimizer = new ClonalGeneration
{
InitialParameters = InitialParameters,
LowerParamBounds = LowerParamBounds,
UpperParamBounds = UpperParamBounds,
Integer = Integer,
FitnessFunction = FitnessFunction,
// Size of the antibody pool.
NumberOfElements = NumberOfElements,
// Number of antibodies selected for cloning.
NumberSelectedForCloning = NumberOfElements / 3,
// Parameter determining the number of clones created for an antibody that was selected for cloning. (0,1]
Beta = beta,
// Number of antibodies created with random parameters in each new generation.
RandomAntibodiesPerGeneration = NumberOfElements / 5,
// Mutation coefficient (0,1].
Ro = ro,
// Stopping criteria.
StoppingType = StoppingType,
// Number of generations.
StoppingNumberOfGenerations = StoppingNumberOfGenerations,
// Number of allowed affinity evaluations.
StoppingNumberOfEvaluations = StoppingNumberOfEvaluations,
// Affinity treshold.
StoppingFitnessTreshold = StoppingFitnessTreshold,
Slow = Slow
};
break;
case 3:
Optimizer = new GeneticAlgorithm
{
InitialParameters = InitialParameters,
LowerParamBounds = LowerParamBounds,
UpperParamBounds = UpperParamBounds,
Integer = Integer,
FitnessFunction = FitnessFunction,
// Size of the individual pool.
NumberOfElements = NumberOfElements,
// Number of parents in each generation.
ParentsInEachGeneration = NumberOfElements / 2,
// The probability of mutation.
MutationProbability = pm,
// The number of crossovers in each generation.
CrossoverPerGeneration = NumberOfElements,
// Number of allowed fitness evaluations.
StoppingNumberOfEvaluations = StoppingNumberOfEvaluations,
// Fitness treshold.
StoppingFitnessTreshold = StoppingFitnessTreshold,
// Number of generations.
StoppingNumberOfGenerations = StoppingNumberOfGenerations,
// Stopping criteria.
StoppingType = StoppingType,
Slow = Slow
};
break;
case 4: //Clonal generation local
Optimizer = new ClonalGenerationLocal
{
InitialParameters = InitialParameters,
LowerParamBounds = LowerParamBounds,
UpperParamBounds = UpperParamBounds,
Integer = Integer,
FitnessFunction = FitnessFunction,
// Size of the antibody pool.
NumberOfElements = NumberOfElements,
// Number of antibodies selected for cloning.
NumberSelectedForCloning = NumberOfElements / 3,
// Parameter determining the number of clones created for an antibody that was selected for cloning. (0,1]
Beta = beta,
// Number of antibodies created with random parameters in each new generation.
RandomAntibodiesPerGeneration = d,
// Mutation coefficient (0,1].
Ro = ro,
// RelativeStepSize
StepSizeRelative = cglssr,
// Local search/get
LocalSearchesPerGeneration = cgln,
// Stopping criteria.
StoppingType = StoppingType,
// Number of generations.
StoppingNumberOfGenerations = StoppingNumberOfGenerations,
// Number of allowed affinity evaluations.
StoppingNumberOfEvaluations = StoppingNumberOfEvaluations,
// Affinity treshold.
StoppingFitnessTreshold = StoppingFitnessTreshold,
Slow = Slow
};
break;
case 5:
Optimizer = new CoordinateDescent
{
InitialParameters = InitialParameters,
LowerParamBounds = LowerParamBounds,
UpperParamBounds = UpperParamBounds,
// Size of the antibody pool.
NumberOfElements = NumberOfElements,
StepSizeRelative = 5,
Integer = Integer,
FitnessFunction = FitnessFunction,
// Number of allowed fitness evaluations.
StoppingNumberOfEvaluations = StoppingNumberOfEvaluations,
// Fitness treshold.
StoppingFitnessTreshold = StoppingFitnessTreshold,
// Number of generations.
StoppingNumberOfGenerations = StoppingNumberOfGenerations,
// Stopping criteria.
StoppingType = StoppingType,
Slow = Slow
};
break;
case 6:
Optimizer = new ArtificialBeeColony
{
InitialParameters = InitialParameters,
LowerParamBounds = LowerParamBounds,
UpperParamBounds = UpperParamBounds,
Integer = Integer,
FitnessFunction = FitnessFunction,
// Size of the antibody pool.
NumberOfElements = NumberOfElements,
//Felderitő méhek maximális keressi számas ciklus alatt
MaxStep = MaxStep,
//Felderítő méhek száma
ExBeeCount = Elite,
// Number of allowed fitness evaluations.
StoppingNumberOfEvaluations = StoppingNumberOfEvaluations,
// Fitness treshold.
StoppingFitnessTreshold = StoppingFitnessTreshold,
// Number of generations.
StoppingNumberOfGenerations = StoppingNumberOfGenerations,
// Stopping criteria.
StoppingType = StoppingType,
Slow = Slow
};
break;
case 7:
Optimizer = new BacterialForaging
{
InitialParameters = InitialParameters,
LowerParamBounds = LowerParamBounds,
UpperParamBounds = UpperParamBounds,
Integer = Integer,
// Size of the antibody pool.
NumberOfElements = NumberOfElements,
ClonesCount = ClonesCount,
Infections = Infections,
FitnessFunction = FitnessFunction,
// Number of allowed fitness evaluations.
StoppingNumberOfEvaluations = StoppingNumberOfEvaluations,
// Fitness treshold.
StoppingFitnessTreshold = StoppingFitnessTreshold,
// Number of generations.
StoppingNumberOfGenerations = StoppingNumberOfGenerations,
// Stopping criteria.
StoppingType = StoppingType,
Slow = Slow
};
break;
case 8:
Optimizer = new DifferentialEvolution
{
InitialParameters = InitialParameters,
LowerParamBounds = LowerParamBounds,
UpperParamBounds = UpperParamBounds,
Integer = Integer,
weighf = weighf,
crossf = crossf,
// Size of the antibody pool.
NumberOfElements = NumberOfElements,
FitnessFunction = FitnessFunction,
// Number of allowed fitness evaluations.
StoppingNumberOfEvaluations = StoppingNumberOfEvaluations,
// Fitness treshold.
StoppingFitnessTreshold = StoppingFitnessTreshold,
// Number of generations.
StoppingNumberOfGenerations = StoppingNumberOfGenerations,
// Stopping criteria.
StoppingType = StoppingType,
Slow = Slow
};
break;
case 9:
Optimizer = new SelfOrgMigrating
{
InitialParameters = InitialParameters,
LowerParamBounds = LowerParamBounds,
UpperParamBounds = UpperParamBounds,
Integer = Integer,
NumberOfElements = NumberOfElements,
PRT = 0.1,
PathLength = 3,
PopSize = 7,
Step = 0.11,
Type = SelfOrgMigratingType.AllToRand,
FitnessFunction = FitnessFunction,
// Number of allowed fitness evaluations.
StoppingNumberOfEvaluations = StoppingNumberOfEvaluations,
// Fitness treshold.
StoppingFitnessTreshold = StoppingFitnessTreshold,
// Number of generations.
StoppingNumberOfGenerations = StoppingNumberOfGenerations,
// Stopping criteria.
StoppingType = StoppingType,
Slow = Slow
};
break;
}
}
