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java.lang.Objectec.BreedingSource
ec.BreedingPipeline
ec.rule.breed.RuleCrossoverPipeline
public class RuleCrossoverPipeline
RuleCrossoverPipeline is a BreedingPipeline which implements a simple default crossover for RuleIndividuals. Normally it takes two individuals and returns two crossed-over child individuals. Optionally, it can take two individuals, cross them over, but throw away the second child (a one-child crossover). RuleCrossoverPipeline works by calling defaultCrossover(...) on the first parent individual.
Typical Number of Individuals Produced Per produce(...) call
1 or 2
Number of Sources
2
Parameters
base.toss bool = true or false (default)/td> | (after crossing over with the first new individual, should its second sibling individual be thrown away instead of adding it to the population?) |
base.prob 0.0 <= float < 1.0, or 0.5 (default)/td> | (probability that a rule will cross over from one individual to the other) |
Default Base
rule.xover
Field Summary | |
---|---|
static int |
INDS_PRODUCED
|
static int |
NUM_SOURCES
|
static java.lang.String |
P_CROSSOVER
|
static java.lang.String |
P_CROSSOVERPROB
|
static java.lang.String |
P_TOSS
|
float |
ruleCrossProbability
What is the probability of a rule migrating? |
boolean |
tossSecondParent
Should the pipeline discard the second parent after crossing over? |
Fields inherited from class ec.BreedingPipeline |
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DYNAMIC_SOURCES, mybase, P_NUMSOURCES, P_SOURCE, sources, V_SAME |
Fields inherited from class ec.BreedingSource |
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CHECKBOUNDARY, DEFAULT_PRODUCED, NO_PROBABILITY, P_PROB, probability, UNUSED |
Constructor Summary | |
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RuleCrossoverPipeline()
|
Method Summary | |
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java.lang.Object |
clone()
Creates a new individual cloned from a prototype, and suitable to begin use in its own evolutionary context. |
Parameter |
defaultBase()
Returns the default base for this prototype. |
int |
numSources()
Returns 2 |
int |
produce(int min,
int max,
int start,
int subpopulation,
Individual[] inds,
EvolutionState state,
int thread)
Produces n individuals from the given subpopulation and puts them into inds[start...start+n-1], where n = Min(Max(q,min),max), where q is the "typical" number of individuals the BreedingSource produces in one shot, and returns n. |
void |
setup(EvolutionState state,
Parameter base)
Sets up the BreedingPipeline. |
int |
typicalIndsProduced()
Returns 2 (unless tossing the second sibling, in which case it returns 1) |
Methods inherited from class ec.BreedingPipeline |
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finishProducing, individualReplaced, maxChildProduction, minChildProduction, preparePipeline, prepareToProduce, produces, sourcesAreProperForm |
Methods inherited from class ec.BreedingSource |
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getProbability, pickRandom, setProbability, setupProbabilities |
Methods inherited from class java.lang.Object |
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equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
Field Detail |
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public static final java.lang.String P_TOSS
public static final java.lang.String P_CROSSOVER
public static final java.lang.String P_CROSSOVERPROB
public static final int INDS_PRODUCED
public static final int NUM_SOURCES
public boolean tossSecondParent
public float ruleCrossProbability
Constructor Detail |
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public RuleCrossoverPipeline()
Method Detail |
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public Parameter defaultBase()
Prototype
public int numSources()
numSources
in class BreedingPipeline
public java.lang.Object clone()
Prototype
Typically this should be a full "deep" clone. However, you may share certain elements with other objects rather than clone hem, depending on the situation:
Implementations.
public Object clone()
{
try
{
return super.clone();
}
catch ((CloneNotSupportedException e)
{ throw new InternalError(); } // never happens
}
public Object clone()
{
try
{
MyObject myobj = (MyObject) (super.clone());
// put your deep-cloning code here...
}
catch ((CloneNotSupportedException e)
{ throw new InternalError(); } // never happens
return myobj;
}
public Object clone()
{
MyObject myobj = (MyObject) (super.clone());
// put your deep-cloning code here...
return myobj;
}
clone
in interface Prototype
clone
in class BreedingPipeline
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 BreedingPipeline
Prototype.setup(EvolutionState,Parameter)
public int typicalIndsProduced()
typicalIndsProduced
in class BreedingPipeline
public int produce(int min, int max, int start, int subpopulation, Individual[] inds, EvolutionState state, int thread)
BreedingSource
produce
in class BreedingSource
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