/** This method will optimize */
@Override
public void optimize() {
AbstractEAIndividual indy;
Population original = (Population) this.population.clone();
double[] fitness;
for (int i = 0; i < this.population.size(); i++) {
indy = this.population.get(i);
double tmpD = indy.getMutationProbability();
indy.setMutationProbability(1.0);
indy.mutate();
indy.setMutationProbability(tmpD);
}
this.optimizationProblem.evaluate(this.population);
for (int i = 0; i < this.population.size(); i++) {
fitness = this.population.get(i).getFitness();
if (fitness[0] > this.currentFloodPeak) {
this.population.remove(i);
this.population.add(i, original.get(i));
}
}
this.currentFloodPeak -= this.drainRate;
this.population.incrGeneration();
this.firePropertyChangedEvent(Population.NEXT_GENERATION_PERFORMED);
}
/**
* This method allows an optimizer to register a change in the optimizer.
*
* @param source The source of the event.
* @param name Could be used to indicate the nature of the event.
*/
@Override
public void registerPopulationStateChanged(Object source, String name) {
if (name.equals(Population.NEXT_GENERATION_PERFORMED)) {
Population population = ((InterfaceOptimizer) source).getPopulation();
double x = 100 / this.multiRuns;
if (this.optimizationParameters.getTerminator() instanceof EvaluationTerminator) {
double y =
x
/ (double)
((EvaluationTerminator) this.optimizationParameters.getTerminator())
.getFitnessCalls();
currentProgress = (int) (this.currentRun * x + population.getFunctionCalls() * y);
} else {
currentProgress = (int) (this.currentRun * x);
}
updateStatus(currentProgress);
// data to be stored in file
double tmpd = 0;
StringBuilder tmpLine = new StringBuilder("");
tmpLine.append(population.getFunctionCalls());
tmpLine.append("\t");
tmpLine.append(population.getBestEAIndividual().getFitness(0));
tmpLine.append("\t");
for (int i = 0; i < population.size(); i++) {
tmpd += population.get(i).getFitness(0) / (double) population.size();
}
tmpLine.append("\t");
tmpLine.append(tmpd);
tmpLine.append("\t");
tmpLine.append(population.getWorstEAIndividual().getFitness(0));
// tmpLine.append("\t");
// tmpLine.append(this.optimizationParameters.getProblem().getAdditionalDataValue(population));
this.writeToFile(tmpLine.toString());
Double[] tmpData = new Double[2];
tmpData[0] = (double) population.getFunctionCalls();
// instead of adding simply the best fitness value i'll ask the problem what to show
tmpData[1] = this.optimizationParameters.getProblem().getDoublePlotValue(population);
if (this.plot != null) {
if (this.continueFlag) {
this.plot.setConnectedPoint(
tmpData[0] + this.recentFunctionCalls, tmpData[1], 1000 + this.currentRun);
} else {
this.plot.setConnectedPoint(tmpData[0], tmpData[1], 1000 + this.currentRun);
}
}
this.tmpData.add(tmpData);
}
}
/**
* This method takes a population of individuals with an array of fitness values and calculates a
* single fitness value to replace the former fitness array. Please note: The orignal fitness
* values are lost this way, so please use the individual.setData() method if you still want to
* access the original fitness values.
*
* @param pop The population to process.
*/
@Override
public void convertMultiObjective2SingleObjective(Population pop) {
for (int i = 0; i < pop.size(); i++) {
this.convertSingleIndividual(pop.get(i));
}
}