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package jcgp.backend.modules.problem;
import java.io.File;
import jcgp.backend.function.FunctionSet;
import jcgp.backend.modules.Module;
import jcgp.backend.parameters.DoubleParameter;
import jcgp.backend.parameters.monitors.DoubleMonitor;
import jcgp.backend.population.Chromosome;
import jcgp.backend.population.Population;
import jcgp.backend.resources.ModifiableResources;
import jcgp.backend.resources.Resources;
/**
* Defines the general behaviour of a CGP problem. The primary function of Problem
* is to evaluate a population and assign a fitness value to each chromosome.
* <br>
* By convention, the population should be sorted into ascending order of fitness. The
* reason for this is because high fitness is not necessarily better - some problem types
* might treat 0 as the best fitness. In order for the evolutionary strategy to be able to
* pick chromosomes by fitness, the safest way is to sort them such that the last chromosome
* is the fittest.
* <br><br>
* When extending this class, the constructor should call a couple methods in order to
* properly construct the problem type: {@code setFunctionSet()} and {@code setFileExtension()},
* with the respective arguments. As with all subclasses of {@code Module}, {@code setName()} and
* {@code registerParameters()} should be used where appropriate as well.
* <br><br>
* It is advisable to use {@code Resources.reportln()} and {@code Resources.report()}
* to print any relevant information. Note that reportln() and report() are affected
* by the report interval base parameter. Use {@code Resources.println()} and
* {@code Resources.print()} to print information regardless of the current generation.
* See {@link Resources} for more information.
*
* @see Module
* @author Eduardo Pedroni
*
*/
public abstract class Problem extends Module {
private FunctionSet functionSet;
private String fileExtension = ".*";
protected DoubleParameter maxFitness, bestFitness;
/**
* Initialises the two problem-wide parameters, maxFitness and bestFitness.
*/
public Problem() {
maxFitness = new DoubleMonitor(0, "Max fitness");
bestFitness = new DoubleMonitor(0, "Best fitness");
registerParameters(maxFitness, bestFitness);
}
/**
* The most important method of the problem type. This is called once
* per generation, when the new population has been generated.
* <br><br>
* The basic functionality of this method is to loop through all chromosomes
* in the population and decode them according to the problem type. The
* fitness of each chromosome is then calculated using the problem data
* or otherwise (subjective problem types such as art generation might
* leave fitness evaluations up to the user) and assigned to the appropriate
* chromosome.
* <br><br>
* In addition, realisations of this method should update the value of
* bestFitness as appropriate, since the value of this parameter is displayed
* if a GUI is in use.
*
* @param population the population to be evaluated.
* @param resources parameters and utilities for optional reference.
*/
public abstract void evaluate(Population population, Resources resources);
/**
* Used to assert whether a given chromosome is a perfect solution
* to the problem. It is up to the problem to define what qualifies
* a perfect solution, as some problems (subject ones such as music and
* art evolution, for example) might not have perfect solutions at all.
* <br><br>
* Note that if this method returns true, the experiment will move on
* to the next run, or finish if no more runs are left.
*
* @param candidate the potentially perfect chromosome.
* @return true if the argument is a perfect solution.
*/
public abstract boolean isPerfectSolution(Chromosome candidate);
/**
* Used to assert whether a given chromosome is an improvement over
* the current best chromosome. A typical implementation of this method
* will simply compare chromosome fitness values, though the problem type
* is free to implement this in any way.
*
* @param candidate the potentially fitter chromosome.
* @return true if the argument is fitter than the currently fittest chromosome.
*/
public abstract boolean isImprovement(Chromosome candidate);
/**
* Parses the specified file and uses the parsed data to
* set up the problem type instance appropriately. Any necessary
* resource changes can be performed using the provided {@code ModifiableResources}
* instance.
* <br><br>
* In addition, realisations of this method should update the value of
* maxFitness where appropriate, as this may be displayed to the user
* if a GUI is in use.
*
* @param file the data file to parse.
* @param resources a modifiable reference to the experiment's resources.
*/
public abstract void parseProblemData(File file, ModifiableResources resources);
/**
* For internal use in subclass constructor, sets the functions to be
* used for this problem type. See {@link FunctionSet} for more details.
*
* @param newFunctionSet the function set to use.
*/
protected void setFunctionSet(FunctionSet newFunctionSet) {
this.functionSet = newFunctionSet;
}
/**
* @return the FunctionSet object used by this problem type.
*/
public FunctionSet getFunctionSet() {
return functionSet;
}
/**
* For internal use in subclass constructors, sets the file extension accepted
* by this problem type's parser. This is used by the GUI to filter loaded files
* by extension in a file chooser. File extensions should be set in the form ".*",
* so for plain text files, ".txt" would be used.
*
* @param fileExtension the accepted file extension.
*/
protected void setFileExtension(String fileExtension) {
this.fileExtension = fileExtension;
}
/**
* @return the file extension accepted by this problem type for problem data files.
*/
public String getFileExtension() {
return fileExtension;
}
/**
* @return the current best fitness, in other words, the fitness
* value of the fittest chromosome in the current generation.
*/
public double getBestFitness() {
return bestFitness.get();
}
/**
* Resets the bestFitness parameter.
*/
public void reset() {
bestFitness.set(0);
}
}
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