public Result Optimize(ArrayList Elements)
{
var Individuals = new ArrayList();
var Fitnesses = new List <Double>();
for (int i = (int)LowerParamBounds[0]; i < (int)UpperParamBounds[0]; i++)
{
for (int j = (int)LowerParamBounds[1]; j < (int)UpperParamBounds[1]; j++)
{
Individuals.Add(new BaseElement(FitnessFunction, new ArrayList {
(double)i, (double)j
}, FitnessEvaluated));
Fitnesses.Add(FitnessFunction(new ArrayList {
(double)i, (double)j
}));
}
}
GenerationCreated?.Invoke(this, Individuals, Fitnesses.ToArray());
return(null);
}
/// <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>
public Result Optimize(ArrayList ElementsTrans)
{
bool genRand = ElementsTrans == null;
// Create a list object for elementes.
Elements = new ArrayList();
if (!genRand)
{
// Transfer elements importing
foreach (BaseElement Element in ElementsTrans)
{
Elements.Add(GetNewElement(FitnessFunction, Element.Position));
}
}
// Create a random number generator object.
RNG = new Random();
// The ordinal number of the actual generation.
Generation = 1;
// Define a logical variable that keeps track of the current state.
Stop = false;
// Start execturion timer
var timer = new System.Diagnostics.Stopwatch();
timer.Reset();
timer.Start();
if (genRand)
{
// Put the first element into the pool.
Elements.Add(GetNewElement(FitnessFunction, InitialParameters));
}
// Initializing the first element takes an evaluation, so we initialize to one
Evaluation = 1;
// Check if the stopping condition was met
if (StopHandler != null)
{
Stop = StopHandler.Invoke(this, Elements);
}
else
{
CheckStop();
}
// Create an object for the returned information.
Info = new Dictionary <InfoTypes, object>();
// Add the affinity (performance) corresponding to the initial parameter values.
Info.Add(InfoTypes.InitialFitness, ((BaseElement)Elements[0]).Fitness);
if (genRand)
{
// Create the rest of the initial pool (first generation) of elementes creating elementes at random positions.
CreateRandomElements(NumberOfElements - 1);
}
// Raise GenerationCreated event if there are any subscribers.
GenerationCreated?.Invoke(this, Elements, GetFitnesses());
ArrayList BestAffinities = new ArrayList();
BestAffinities.Add(((BaseElement)Elements[0]).Fitness);
if (StopHandler != null)
{
Stop = StopHandler.Invoke(this, Elements);
}
else
{
CheckStop();
}
//Begin the optimization process
while (!Stop)
{
if (Slow)
{
System.Threading.Thread.Sleep(100);
}
Generation++;
// Create next generation.
CreateNextGeneration();
BestAffinities.Add(((BaseElement)Elements[0]).Fitness);
// Raise GenerationCreated event if there are any subscribers.
GenerationCreated?.Invoke(this, Elements, GetFitnesses());
// Stop if the stopping criteria is the number of generations and the number of allowed generations is reached
if (StopHandler != null)
{
Stop = StopHandler.Invoke(this, Elements);
}
else
{
CheckStop();
}
}
// Stop timer
timer.Stop();
// Create an array for the parameters (position) of the best antibody.
var Best = new ArrayList();
for (int dim = 0; dim < InitialParameters.Count; dim++)
{
Best.Add(((BaseElement)Elements[0])[dim]);
}
// Add the affinity (performance) value of the best antibody to the returned information.
Info.Add(InfoTypes.FinalFitness, ((BaseElement)Elements[0]).Fitness);
// Add munber of generations.
Info.Add(InfoTypes.Generations, Generation);
// Add number of affinity evaluations.
Info.Add(InfoTypes.Evaluations, Evaluation);
// Define returned values.
Info.Add(InfoTypes.Affinities, BestAffinities);
// Add execution time to result info
Info.Add(InfoTypes.ExecutionTime, timer.ElapsedMilliseconds);
var res = new Result(Best, Info);
return(res);
}
