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java.lang.Objectec.BreedingSource
ec.BreedingPipeline
ec.vector.breed.ListCrossoverPipeline
public class ListCrossoverPipeline
ListCrossoverPipeline is a crossover pipeline for vector individuals whose length may be lengthened or shortened. There are two crossover options available: one-point and two-point. One-point crossover picks a crossover point for each of the vectors (the crossover point can be different), and then does one-point crossover using those points. Two-point crossover picks TWO crossover points for each of the vectors (again, the points can be different among the vectors), and swaps the middle regions between the respective crossover points.
ListCrossoverPipeline will try tries times to meet certain constraints: first, the resulting children must be no smaller than min-child-size. Second, the amount of material removed from a parent must be no less than mix-crossover-percent and no more than max-crossover-percent.
If toss is true, then only one child is generated, else at most two are generated.
Typical Number of Individuals Produced Per produce(...) call
2 * minimum typical number of individuals produced by each source, unless toss
is set, in which case it's simply the minimum typical number.
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.tries int >= 1 |
(number of times to try finding valid crossover points) |
base.min-child-size int >= 0 (default) |
(the minimum allowed size of a child) |
base.min-crossover-percent 0 (default) <= float <= 1 |
(the minimum percentage of an individual that may be removed during crossover) |
base.max-crossover-percent 0 <= float <= 1 (default) |
(the maximum percentage of an individual that may be removed during crossover) |
Default Base
vector.list-xover
Field Summary | |
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int |
crossoverType
|
float |
maxCrossoverPercentage
|
int |
minChildSize
|
float |
minCrossoverPercentage
|
static int |
NUM_SOURCES
|
int |
numTries
|
static java.lang.String |
P_LIST_CROSSOVER
|
static java.lang.String |
P_MAX_CROSSOVER_PERCENT
|
static java.lang.String |
P_MIN_CHILD_SIZE
|
static java.lang.String |
P_MIN_CROSSOVER_PERCENT
|
static java.lang.String |
P_NUM_TRIES
|
static java.lang.String |
P_TOSS
|
boolean |
tossSecondParent
|
Fields inherited from class ec.BreedingPipeline |
---|
DYNAMIC_SOURCES, likelihood, mybase, P_LIKELIHOOD, P_NUMSOURCES, P_SOURCE, sources, V_SAME |
Fields inherited from class ec.BreedingSource |
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NO_PROBABILITY, P_PROB, probability |
Constructor Summary | |
---|---|
ListCrossoverPipeline()
|
Method Summary | |
---|---|
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 the number of sources to this pipeline. |
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 the "typical" number of individuals produced -- by default this is the minimum typical number of individuals produced by any children sources of the pipeline. |
Methods inherited from class ec.BreedingPipeline |
---|
finishProducing, individualReplaced, maxChildProduction, minChildProduction, preparePipeline, prepareToProduce, produces, reproduce, sourcesAreProperForm |
Methods inherited from class ec.BreedingSource |
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getProbability, pickRandom, setProbability, setupProbabilities |
Methods inherited from class java.lang.Object |
---|
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_LIST_CROSSOVER
public static final java.lang.String P_MIN_CHILD_SIZE
public static final java.lang.String P_NUM_TRIES
public static final java.lang.String P_MIN_CROSSOVER_PERCENT
public static final java.lang.String P_MAX_CROSSOVER_PERCENT
public static final int NUM_SOURCES
public boolean tossSecondParent
public int crossoverType
public int minChildSize
public int numTries
public float minCrossoverPercentage
public float maxCrossoverPercentage
Constructor Detail |
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public ListCrossoverPipeline()
Method Detail |
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public Parameter defaultBase()
Prototype
public int numSources()
BreedingPipeline
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()
BreedingPipeline
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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