public class GreedyOverselection extends SelectionMethod
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
Modifier and Type | Field and Description |
---|---|
double |
gets_n_percent |
static java.lang.String |
P_GETS |
static java.lang.String |
P_GREEDY |
static java.lang.String |
P_TOP |
double[] |
sortedFitOver |
double[] |
sortedFitUnder |
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.
|
double |
top_n_percent |
INDS_PRODUCED, KEY_PARENTS
NO_PROBABILITY, P_PROB, probability
Constructor and Description |
---|
GreedyOverselection() |
Modifier and Type | Method and Description |
---|---|
Parameter |
defaultBase()
Returns the default base for this prototype.
|
void |
finishProducing(EvolutionState s,
int subpopulation,
int thread)
A default version of finishProducing, which does nothing.
|
void |
prepareToProduce(EvolutionState s,
int subpopulation,
int thread)
A default version of prepareToProduce which does nothing.
|
int |
produce(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.
|
void |
setup(EvolutionState state,
Parameter base)
Sets up the BreedingPipeline.
|
produce, produces, produceWithoutCloning, typicalIndsProduced
clone, fillStubs, getProbability, pickRandom, preparePipeline, setProbability, setupProbabilities
public double[] sortedFitOver
public double[] sortedFitUnder
public int[] sortedPop
public static final java.lang.String P_GREEDY
public static final java.lang.String P_TOP
public static final java.lang.String P_GETS
public double top_n_percent
public double gets_n_percent
public Parameter defaultBase()
Prototype
public void setup(EvolutionState state, Parameter base)
BreedingSource
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}.
setup
in interface Prototype
setup
in interface Setup
setup
in class BreedingSource
Prototype.setup(EvolutionState,Parameter)
public void prepareToProduce(EvolutionState s, int subpopulation, int thread)
SelectionMethod
prepareToProduce
in class SelectionMethod
public int produce(int subpopulation, EvolutionState state, int thread)
SelectionMethod
produce
in class SelectionMethod
public void finishProducing(EvolutionState s, int subpopulation, int thread)
SelectionMethod
finishProducing
in class SelectionMethod