From 8189116ea4b5db4675e31dfd04a5687d55e29262 Mon Sep 17 00:00:00 2001 From: Eduardo Pedroni Date: Tue, 6 May 2014 14:29:37 +0100 Subject: Added javadocs, made minor changes to the comments --- src/jcgp/backend/population/Chromosome.java | 7 ++- src/jcgp/backend/population/MutableElement.java | 2 +- src/jcgp/backend/population/Population.java | 64 ++++++------------------- 3 files changed, 20 insertions(+), 53 deletions(-) (limited to 'src/jcgp/backend/population') diff --git a/src/jcgp/backend/population/Chromosome.java b/src/jcgp/backend/population/Chromosome.java index b99b817..0dfa801 100644 --- a/src/jcgp/backend/population/Chromosome.java +++ b/src/jcgp/backend/population/Chromosome.java @@ -159,7 +159,8 @@ public class Chromosome implements Comparable { * this instance. In practice, this iterates through the * entire chromosome making equivalent connections and * setting functions to the same values as those in the - * specified chromosome. + * specified chromosome. It also sets the fitness of the + * copy to the same value as the original. *
* It is assumed that both chromosomes have the same * topology; while this method will still run if that is not @@ -205,6 +206,9 @@ public class Chromosome implements Comparable { System.out.println("Warning: Connection of subtype " + copyOutput.getClass().toString() + " is not explicitly handled by copy constructor."); } } + + // copy fitness as well + this.fitness = clone.getFitness(); } /** @@ -264,7 +268,6 @@ public class Chromosome implements Comparable { * number of inputs exactly, an exception is thrown. * * @param values the values the input should take. - * @throws ParameterMismatchException if the wrong number of values is received. */ public void setInputs(Object ... values) { // if the values provided don't match the specified number of inputs, the user should be warned diff --git a/src/jcgp/backend/population/MutableElement.java b/src/jcgp/backend/population/MutableElement.java index 33f3890..5782a99 100644 --- a/src/jcgp/backend/population/MutableElement.java +++ b/src/jcgp/backend/population/MutableElement.java @@ -44,7 +44,7 @@ public interface MutableElement { *
  • not reflexive: a.copyOf(a) returns false;
    • *
  • not transitive: if a.copyOf(b) is true and b.copyOf(c) is true, a.copyOf(c) is * not necessarily true since it is possible that a == c.
    • - * + * * @param element the mutable element to compare to. * @return true if {@code element} is a copy of this element. */ diff --git a/src/jcgp/backend/population/Population.java b/src/jcgp/backend/population/Population.java index 5d549e9..70cfda8 100644 --- a/src/jcgp/backend/population/Population.java +++ b/src/jcgp/backend/population/Population.java @@ -3,6 +3,7 @@ package jcgp.backend.population; import java.util.Arrays; import java.util.Collections; +import jcgp.backend.modules.problem.BestFitness; import jcgp.backend.resources.Resources; /** @@ -19,16 +20,6 @@ import jcgp.backend.resources.Resources; * experiment's specified seed. If an entirely random population * is needed, {@code reinitialise()} should be used to randomise * all chromosomes without creating a new instance of {@code Population}. - *

    - * By convention the population's chromosomes should always be sorted in - * order of fitness, from worst to best. This cannot be done automatically - * since a higher fitness value does not necessarily mean better fitness; - * some problem types might instead interpret fitness 0 as a perfect solution. - * Sorting the population is done easily using {@code sortAscending()} and - * {@code sortDescending()}, and should be done by the problem type as appropriate. - * It is critical to sort the population after it is evaluated as - * evolutionary strategies will obey the convention and assume the population - * is sorted in order of fitness. * * * @author Eduardo Pedroni @@ -75,7 +66,7 @@ public class Population { * @param index the chromosome to return. * @return the indexed chromosome. */ - public Chromosome getChromosome(int index) { + public Chromosome get(int index) { return chromosomes[index]; } @@ -112,46 +103,19 @@ public class Population { chromosomes[c].reinitialiseConnections(); } } - - /** - * The fittest chromosome, by convention, is the last one - * in the array. Problem evaluation methods are expected to - * sort the population into ascending order of fitness, such - * that the best chromosome is in the last position (size - 1). - * This method assumes that the population is sorted as such - * and returns the last chromosome in the array. - * - * @return the fittest chromosome in the population. - */ - public Chromosome getFittest() { - return chromosomes[chromosomes.length - 1]; - } - - /** - * The fittest chromosome, by convention, is the last one - * in the array. Problem evaluation methods are expected to - * sort the population into ascending order of fitness, such - * that the best chromosome is in the last position (size - 1). - * This method assumes that the population is sorted as such - * and returns the last index in the array. - * - * @return the index of the fittest chromosome. - */ - public int getFittestIndex() { - return chromosomes.length - 1; - } - - /** - * Sort the population into ascending order of fitness. - */ - public void sortAscending() { - Arrays.sort(chromosomes); - } - + /** - * Sort the population into descending order of fitness. + * Sorts the population in ascending order of fitness quality. + * What this means is that the best fitness chromosome will be + * in the last position, even though it might have the lowest + * fitness value. Fitness orientation as specified in the resources + * is respected. */ - public void sortDescending() { - Arrays.sort(chromosomes, Collections.reverseOrder()); + public void sort() { + if (resources.fitnessOrientation() == BestFitness.HIGH) { + Arrays.sort(chromosomes); + } else { + Arrays.sort(chromosomes, Collections.reverseOrder()); + } } } -- cgit v1.2.3