Documentation of 'ec.select.GreedyOverselection' Java class
GreedyOverselection
ec.select

Class GreedyOverselection

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


    public class GreedyOverselection
    extends SelectionMethod
    GreedyOverselection is a SelectionMethod which implements Koza-style fitness-proportionate greedy overselection. Not appropriate for multiobjective fitnesses.

    This selection method first divides individuals in a population into two groups: the "good" ("top") group, and the "bad" ("bottom") group. The best top percent of individuals in the population go into the good group. The rest go into the "bad" group. With a certain probability (determined by the gets setting), an individual will be picked out of the "good" group. Once we have determined which group the individual will be selected from, the individual is picked using fitness proportionate selection in that group, that is, the likelihood he is picked is proportionate to his fitness relative to the fitnesses of others in his group.

    All this is expensive to set up and bring down, so it's not appropriate for steady-state evolution. If you're not familiar with the relative advantages of selection methods and just want a good one, use TournamentSelection instead.

    Note: Fitnesses must be non-negative. 0 is assumed to be the worst fitness.

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

    Parameters

    base.top
    0.0 <= double <= 1.0
    (the percentage of the population going into the "good" (top) group)
    base.gets
    0.0 <= double <= 1.0
    (the likelihood that an individual will be picked from the "good" group)

    Default Base
    select.greedy

    See Also:
    Serialized Form
    • Field Detail

      • sortedFitOver

        public double[] sortedFitOver
      • sortedFitUnder

        public double[] sortedFitUnder
      • sortedPop

        public int[] sortedPop
        Sorted population -- since I *have* to use an int-sized individual (short gives me only 16K), I might as well just have pointers to the population itself. :-(
      • top_n_percent

        public double top_n_percent
      • gets_n_percent

        public double gets_n_percent
    • Constructor Detail

      • GreedyOverselection

        public GreedyOverselection()
    • 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(...).
      • 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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