Class GreedyOverselection
- All Implemented Interfaces:
Prototype,Setup,RandomChoiceChooserD,Serializable,Cloneable
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
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Field Summary
FieldsModifier and TypeFieldDescriptiondoublestatic final Stringstatic final Stringstatic final Stringdouble[]double[]int[]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.doubleFields inherited from class ec.SelectionMethod
INDS_PRODUCED, KEY_PARENTSFields inherited from class ec.BreedingSource
NO_PROBABILITY, P_PROB, probability -
Constructor Summary
Constructors -
Method Summary
Modifier and TypeMethodDescriptionReturns the default base for this prototype.voidfinishProducing(EvolutionState s, int subpopulation, int thread) A default version of finishProducing, which does nothing.voidprepareToProduce(EvolutionState s, int subpopulation, int thread) A default version of prepareToProduce which does nothing.intproduce(int subpopulation, EvolutionState state, int thread) An alternative form of "produce" special to Selection Methods; selects an individual from the given subpopulation and returns its position in that subpopulation.voidsetup(EvolutionState state, Parameter base) Sets up the BreedingPipeline.Methods inherited from class ec.SelectionMethod
produce, produces, produceWithoutCloning, typicalIndsProducedMethods inherited from class ec.BreedingSource
clone, fillStubs, getProbability, pickRandom, preparePipeline, setProbability, setupProbabilities
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Field Details
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sortedFitOver
public double[] sortedFitOver -
sortedFitUnder
public double[] sortedFitUnder -
sortedPop
public int[] sortedPopSorted 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. :-( -
P_GREEDY
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P_TOP
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P_GETS
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top_n_percent
public double top_n_percent -
gets_n_percent
public double gets_n_percent
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Constructor Details
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GreedyOverselection
public GreedyOverselection()
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Method Details
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defaultBase
Description copied from interface:PrototypeReturns 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
Description copied from class:BreedingSourceSets 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}.
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prepareToProduce
Description copied from class:SelectionMethodA default version of prepareToProduce which does nothing.- Overrides:
prepareToProducein classSelectionMethod
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produce
Description copied from class:SelectionMethodAn alternative form of "produce" special to Selection Methods; selects an individual from the given subpopulation and returns its position in that subpopulation.- Specified by:
producein classSelectionMethod
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finishProducing
Description copied from class:SelectionMethodA default version of finishProducing, which does nothing.- Overrides:
finishProducingin classSelectionMethod
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