diff options
Diffstat (limited to 'src/jcgp/backend/modules/problem/Problem.java')
| -rw-r--r-- | src/jcgp/backend/modules/problem/Problem.java | 57 |
1 files changed, 36 insertions, 21 deletions
diff --git a/src/jcgp/backend/modules/problem/Problem.java b/src/jcgp/backend/modules/problem/Problem.java index 721b9b3..5f194b5 100644 --- a/src/jcgp/backend/modules/problem/Problem.java +++ b/src/jcgp/backend/modules/problem/Problem.java @@ -6,23 +6,21 @@ 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 + * Defines the general behaviour of a CGP problem. The primary function of {@code 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. + * Problems are free to define whether better fitness means a higher or lower fitness value. + * In some problem types, it is more convenient to treat fitness 0 as the best possible value. + * This can be done by changing the fitness orientation to {@code BestFitness.HIGH} or {@code BestFitness.LOW} as appropriate. + * Fitness orientation is set to high by default. * <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()}, + * When extending this class, the constructor should call a few methods in order to + * properly construct the problem type: {@code setFunctionSet()}, {@code setFileExtension()} and {@code setFitnessOrientation()}, * 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> @@ -41,12 +39,18 @@ public abstract class Problem extends Module { private FunctionSet functionSet; private String fileExtension = ".*"; + private BestFitness fitnessOrientation = BestFitness.HIGH; + protected DoubleParameter maxFitness, bestFitness; /** * Initialises the two problem-wide parameters, maxFitness and bestFitness. + * + * @param resources a reference to the experiment's resources. */ - public Problem() { + public Problem(Resources resources) { + super(resources); + maxFitness = new DoubleMonitor(0, "Max fitness"); bestFitness = new DoubleMonitor(0, "Best fitness"); registerParameters(maxFitness, bestFitness); @@ -73,29 +77,26 @@ public abstract class Problem extends Module { public abstract void evaluate(Population population, Resources resources); /** - * Used to assert whether a given chromosome is a perfect solution + * Used to assert whether a given population contains 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. + * @param population the population to search through for a perfect chromosome. + * @return the perfect solution index, if one exits, -1 if no perfect solution was found. */ - public abstract boolean isPerfectSolution(Chromosome candidate); + public abstract int perfectSolutionFound(Population population); /** - * Used to assert whether a given chromosome is an improvement over + * Used to assert whether a given population has a chromosome that 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. + * @param population the population potentially containing a fitter chromosome. + * @return the index of the first chromosome in the population that is an improvement, -1 if none is found. */ - public abstract boolean isImprovement(Chromosome candidate); + public abstract int hasImprovement(Population population); /** * Parses the specified file and uses the parsed data to @@ -149,6 +150,20 @@ public abstract class Problem extends Module { } /** + * @param newOrientation the new fitness orientation to set. + */ + protected void setFitnessOrientation(BestFitness newOrientation) { + this.fitnessOrientation = newOrientation; + } + + /** + * @return the fitness orientation of this particular problem. + */ + public BestFitness getFitnessOrientation() { + return fitnessOrientation; + } + + /** * @return the current best fitness, in other words, the fitness * value of the fittest chromosome in the current generation. */ |