public EvolutionaryProgramming(CreateSolutionMethod solution_generator, int dimension, int pop_size = 100, int bout_size = 5)
{
mPopSize = pop_size;
mDimension = dimension;
mBoutSize = bout_size;
mSolutionGenerator = solution_generator;
if (mSolutionGenerator == null)
{
throw new NullReferenceException();
}
}
public EvolutionStrategy(CreateSolutionMethod solution_generator, int dimension, int mu = 30, int lambda = 20)
{
mPopSize_mu = mu;
mOffspringSize_lambda = lambda;
mDimension = dimension;
mSolutionGenerator = solution_generator;
if (mSolutionGenerator == null)
{
throw new NullReferenceException();
}
}
public DifferentialEvolution(CostFunction f)
{
mSolutionGenerator = (dimension, constraints) =>
{
double[] x_0 = f.CreateRandomSolution();
return(x_0);
};
mPopSize = f.DimensionCount * 10;
mDimension = f.DimensionCount;
mUpperBounds = f.UpperBounds;
mLowerBounds = f.LowerBounds;
}
public EvolutionaryProgramming(CostFunction f, int popSize = 100, int boutSize = 5)
{
mSolutionGenerator = (dimension, constraints) =>
{
double[] x_0 = f.CreateRandomSolution();
return(x_0);
};
mPopSize = popSize;
mDimension = f.DimensionCount;
mBoutSize = boutSize;
mUpperBounds = f.UpperBounds;
mLowerBounds = f.LowerBounds;
}
public EvolutionStrategy(CostFunction f, int mu = 30, int lambda = 20)
{
mSolutionGenerator = (dimension, constraints) =>
{
double[] x_0 = f.CreateRandomSolution();
return(x_0);
};
mPopSize_mu = mu;
mOffspringSize_lambda = lambda;
mDimension = f.DimensionCount;
mUpperBounds = f.UpperBounds;
mLowerBounds = f.LowerBounds;
}
public DifferentialEvolution(int dimension, CreateSolutionMethod solution_generator)
{
mPopSize = dimension * 10;
}