public class MutateAllNodesPipeline extends GPBreedingPipeline
MutateAllNodesPipeline chooses a subtree and for each node n in that subtree, it replaces n with a randomly-picked node of the same arity and type constraints. Thus the original topological structure is the same but the nodes are different.
Typical Number of Individuals Produced Per produce(...) call
...as many as the source produces
Number of Sources
1
Parameters
base.ns.0 classname, inherits and != GPNodeSelector |
(GPNodeSelector for tree) |
base.tree.0 0 < int < (num trees in individuals), if exists |
(tree chosen for mutation; if parameter doesn't exist, tree is picked at random) |
Default Base
gp.breed.mutate-all-nodes
Parameter bases
base.ns | The GPNodeSelector selector |
Modifier and Type | Field and Description |
---|---|
static String |
KEY_PARENTS |
GPNodeSelector |
nodeselect
How the pipeline chooses a subtree to mutate
|
static int |
NUM_SOURCES |
static String |
P_MUTATEALLNODES |
private static long |
serialVersionUID |
(package private) int |
tree
Is our tree fixed? If not, this is -1
|
P_NODESELECTOR, P_TREE, TREE_UNFIXED
DYNAMIC_SOURCES, likelihood, mybase, P_LIKELIHOOD, P_NUMSOURCES, P_SOURCE, sources, V_SAME, V_STUB
NO_PROBABILITY, P_PROB, probability
Constructor and Description |
---|
MutateAllNodesPipeline() |
Modifier and Type | Method and Description |
---|---|
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.
|
private GPNode |
generateCompatibleTree(GPNode original,
GPFunctionSet set,
EvolutionState state,
GPType returntype,
int thread)
Returns a brand-new tree which is swap-compatible with returntype, created by making nodes "compatible" with the equivalent nodes in the tree rooted at original.
|
int |
numSources()
Returns the number of sources to this pipeline.
|
private GPNode |
pickCompatibleNode(GPNode original,
GPFunctionSet set,
EvolutionState state,
GPType returntype,
int thread)
Returns a node which is swap-compatible with returntype, and whose arguments are swap-compatible with the current children of original.
|
int |
produce(int min,
int max,
int subpopulation,
ArrayList<Individual> inds,
EvolutionState state,
int thread,
HashMap<String,Object> misc)
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.
|
produces
fillStubs, finishProducing, individualReplaced, maxChildProduction, minChildProduction, preparePipeline, prepareToProduce, sourcesAreProperForm, typicalIndsProduced
getProbability, pickRandom, setProbability, setupProbabilities
private static final long serialVersionUID
public static final String P_MUTATEALLNODES
public static final int NUM_SOURCES
public static final String KEY_PARENTS
public GPNodeSelector nodeselect
int tree
public Parameter defaultBase()
Prototype
public int numSources()
BreedingPipeline
numSources
in class BreedingPipeline
public 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)
private GPNode pickCompatibleNode(GPNode original, GPFunctionSet set, EvolutionState state, GPType returntype, int thread)
private GPNode generateCompatibleTree(GPNode original, GPFunctionSet set, EvolutionState state, GPType returntype, int thread)
public int produce(int min, int max, int subpopulation, ArrayList<Individual> inds, EvolutionState state, int thread, HashMap<String,Object> misc)
BreedingSource
produce
in class BreedingSource
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