/**
* Evaluates a solution
*
* @param z The solution to evaluate
* @return a double [] with the evaluation results
*/
@Override
public float[] evaluate(float[] z) {
float[] y;
y = normalise(z);
y = t1(y, k_);
y = t2(y, k_);
try {
y = t3(y);
} catch (JMException e) {
// TODO Auto-generated catch block
e.printStackTrace();
}
y = t4(y, k_, M_);
float[] result = new float[M_];
float[] x = calculate_x(y);
for (int m = 1; m <= M_ - 1; m++) {
result[m - 1] = D_ * x[M_ - 1] + S_[m - 1] * (new Shapes()).convex(x, m);
}
result[M_ - 1] = D_ * x[M_ - 1] + S_[M_ - 1] * (new Shapes()).mixed(x, 5, (float) 1.0);
return result;
} // evaluate
/** Constructor */
public SPEA2_Settings(String problem) {
super(problem);
Object[] problemParams = {"Real"};
try {
problem_ = (new ProblemFactory()).getProblem(problemName_, problemParams);
} catch (JMException e) {
// TODO Auto-generated catch block
e.printStackTrace();
}
populationSize_ = 100;
archiveSize_ = 100;
maxEvaluations_ = 25000;
mutationProbability_ = 1.0 / problem_.getNumberOfVariables();
crossoverProbability_ = 0.9;
crossoverDistributionIndex_ = 20.0;
mutationDistributionIndex_ = 20.0;
} // SPEA2_Settings
/** Constructor */
public CellDE_Settings(String problemName) {
super(problemName);
Object[] problemParams = {"Real"};
try {
problem_ = (new ProblemFactory()).getProblem(problemName_, problemParams);
} catch (JMException e) {
// TODO Auto-generated catch block
e.printStackTrace();
}
// Default experiments.settings
CR_ = 0.5;
F_ = 0.5;
populationSize_ = 100;
archiveSize_ = 100;
maxEvaluations_ = 25000;
archiveFeedback_ = 20;
} // CellDE_Settings