Documentation of 'ec.parsimony.RatioBucketTournamentSelection' Java class
RatioBucketTournamentSelection
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ec.parsimony

Class RatioBucketTournamentSelection

  • All Implemented Interfaces:
    Prototype, Setup, SteadyStateBSourceForm, RandomChoiceChooserD, java.io.Serializable, java.lang.Cloneable

    public class RatioBucketTournamentSelection
extends SelectionMethod
implements SteadyStateBSourceForm
    Does a tournament selection, limited to the subpopulation it's working in at the time.

    Ratio Bucket Lexicographic Tournament selection works like as follows. The sizes of buckets are proportioned so that low-fitness individuals are placed into much larger buckets than high-fitness individuals. A bucket ratio 1/ratio is specified beforehand. The bottom 1/ratio individuals of the population are placed into the bottom bucket. If any individuals remain in the population with the same fitness as the best individual in the bottom bucket, they too are placed in that bucket. Of the remaining population, the next 1/ratio individuals are placed into the next bucket, plus any individuals remaining in the population with the same fitness as the best individual now in that bucket, and so on. This continues until every member of the population has been placed in a bucket. Once again, the fitness of every individual in a bucket is set to the rank of the bucket relative to other buckets. Ratio bucketing thus allows parsimony to have more of an effect on average when two similar low-fitness individuals are considered than when two high-fitness individuals are considered. After ranking the individuals, size individuals are chosen at random from the population. Of those individuals, the one with the highest rank is selected. If the two individuals are in the same rank, meaning that they have similar fitness, the one with the smallest size is selected.

    Bucket Lexicographic Tournament selection is so simple that it doesn't need to maintain a cache of any form, so many of the SelectionMethod methods just don't do anything at all.

    Typical Number of Individuals Produced Per produce(...) call
    Always 1.

    Parameters

    base.size
    int >= 1 (default 7)    		 (the tournament size)
    base.pick-worst
    bool = true or false (default)    		 (should we pick the worst individual in the tournament instead of the best?)
    base.ratio
    double >= 2 (default)    		 (the ratio of worst out of remaining individuals that go in the next bucket)

    Default Base
    select.ratio-bucket-tournament

    See Also:
    Serialized Form

    • Field Detail

      • P_RATIO_BUCKET_TOURNAMENT

        public static final java.lang.String P_RATIO_BUCKET_TOURNAMENT
        default base
        See Also:
        Constant Field Values

      • size

        public int size
        Size of the tournament

      • P_PICKWORST

        public static final java.lang.String P_PICKWORST
        if the worst individual should be picked in the tournament
        See Also:
        Constant Field Values

      • pickWorst

        public boolean pickWorst
        Do we pick the worst instead of the best?

      • P_RATIO

        public static final java.lang.String P_RATIO
        The value of RATIO: each step, the worse 1/RATIO individuals are assigned the same fitness
        See Also:
        Constant Field Values

      • ratio

        public double ratio
        The value of RATIO

    • Constructor Detail

      • RatioBucketTournamentSelection

        public RatioBucketTournamentSelection()

    • Method Detail

      • defaultBase

        public Parameter defaultBase()
        Description copied from interface: Prototype
        Returns the default base for this prototype. This should generally be implemented by building off of the static base() method on the DefaultsForm object for the prototype's package. This should be callable during setup(...).
        Specified by:
        defaultBase in interface Prototype

      • setup

        public void setup(EvolutionState state,
                  Parameter base)
        Description copied from class: BreedingSource
        Sets up the BreedingPipeline. You can use state.output.error here because the top-level caller promises to call exitIfErrors() after calling setup. Note that probability might get modified again by an external source if it doesn't normalize right.

        The most common modification is to normalize it with some other set of probabilities, then set all of them up in increasing summation; this allows the use of the fast static BreedingSource-picking utility method, BreedingSource.pickRandom(...). In order to use this method, for example, if four breeding source probabilities are {0.3, 0.2, 0.1, 0.4}, then they should get normalized and summed by the outside owners as: {0.3, 0.5, 0.6, 1.0}.

        Specified by:
        setup in interface Prototype
        Specified by:
        setup in interface Setup
        Overrides:
        setup in class BreedingSource
        See Also:
        Prototype.setup(EvolutionState,Parameter)

      • produce

        public int produce(int subpopulation,
                   EvolutionState state,
                   int thread)
        Description copied from class: SelectionMethod
        An alternative form of "produce" special to Selection Methods; selects an individual from the given subpopulation and returns its position in that subpopulation.
        Specified by:
        produce in class SelectionMethod
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