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java.lang.Object | +--ec.BreedingSource | +--ec.BreedingPipeline | +--ec.gp.GPBreedingPipeline | +--ec.gp.breed.MutateOneNodePipeline
MutateOneNodesPipeline implements the OneNode mutation algorithm described in Kumar Chellapilla, "A Preliminary Investigation into Evolving Modular Programs without Subtree Crossover", GP98.
MutateOneNodesPipeline chooses a single node in an individual and replaces it with a randomly-chosen node of the same arity and type constraints. Thus the original topological structure is the same but that one node is different.
Typical Number of Individuals Produced Per produce(...) call
1
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-one-node
Parameter bases
base.ns | The GPNodeSelector selector |
Field Summary | |
static int |
INDS_PRODUCED
|
GPNodeSelector |
nodeselect
How the pipeline chooses a subtree to mutate |
static int |
NUM_SOURCES
|
static java.lang.String |
P_MUTATEONENODE
|
Fields inherited from class ec.gp.GPBreedingPipeline |
P_NODESELECTOR,
P_TREE,
TREE_UNFIXED |
Fields inherited from class ec.BreedingPipeline |
DYNAMIC_SOURCES,
P_NUMSOURCES,
P_SOURCE,
sources,
V_SAME |
Fields inherited from class ec.BreedingSource |
CHECKBOUNDARY,
DEFAULT_PRODUCED,
NO_PROBABILITY,
P_PROB,
probability,
UNUSED |
Constructor Summary | |
MutateOneNodePipeline()
|
Method Summary | |
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. |
java.lang.Object |
protoClone()
Creates a new individual cloned from a prototype, and suitable to begin use in its own evolutionary context. |
void |
setup(EvolutionState state,
Parameter base)
Sets up the BreedingPipeline. |
int |
typicalIndsProduced()
Returns 1 |
Methods inherited from class ec.gp.GPBreedingPipeline |
produces |
Methods inherited from class ec.BreedingPipeline |
preparePipeline,
prepareToProduce |
Methods inherited from class ec.BreedingSource |
getProbability,
pickRandom,
protoCloneSimple,
setProbability,
setupProbabilities |
Methods inherited from class java.lang.Object |
clone,
equals,
finalize,
getClass,
hashCode,
notify,
notifyAll,
toString,
wait,
wait,
wait |
Field Detail |
public static final java.lang.String P_MUTATEONENODE
public static final int INDS_PRODUCED
public static final int NUM_SOURCES
public GPNodeSelector nodeselect
Constructor Detail |
public MutateOneNodePipeline()
Method Detail |
public Parameter defaultBase()
public int numSources()
public java.lang.Object protoClone() throws java.lang.CloneNotSupportedException
The question here is whether or not this means to perform a "deep" or "light" ("shallow") clone, or something in-between. You may need to deep-clone parts of your object rather than simply copying their references, depending on the situation:
Implementations.
public Object protoClone() throws CloneNotSupportedException
{
return super.clone();
}
public Object protoClone() throws CloneNotSupportedException
{
myobj = (MyObject) (super.clone());
// put your deep-cloning code here...
// ...you should use protoClone and not
// protoCloneSimple to clone subordinate objects...
return myobj;
}
public Object protoClone() throws CloneNotSupportedException
{
MyObject myobj = (MyObject)(super.protoClone());
// put your deep-cloning code here...
// ...you should use protoClone and not
// protoCloneSimple to clone subordinate objects...
return myobj;
}
If you know that your superclasses will never change their protoClone() implementations, you might try inlining them in your overridden protoClone() method. But this is dangerous (though it yields a small net increase).
In general, you want to keep your deep cloning to an absolute minimum, so that you don't have to call protoClone() but one time.
The approach taken here is the fastest that I am aware of while still permitting objects to be specified at runtime from a parameter file. It would be faster to use the "new" operator; but that would require hard-coding that we can't do. Although using java.lang.Object.clone() entails an extra layer that deals with stripping away the "protected" keyword and also wrapping the exception handling (which is a BIG hit, about three times as slow as using "new"), it's still MUCH faster than using java.lang.Class.newInstance(), and also much faster than rolling our own Clone() method.
public void setup(EvolutionState state, Parameter base)
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}.
Prototype.setup(EvolutionState,Parameter)
public int typicalIndsProduced()
public int produce(int min, int max, int start, int subpopulation, Individual[] inds, EvolutionState state, int thread) throws java.lang.CloneNotSupportedException
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