|
|||||||||
PREV CLASS NEXT CLASS | FRAMES NO FRAMES | ||||||||
SUMMARY: INNER | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
java.lang.Object | +--ec.BreedingSource | +--ec.BreedingPipeline | +--ec.gp.GPBreedingPipeline | +--ec.gp.koza.MutationPipeline
MutationPipeline is a GPBreedingPipeline which implements a strongly-typed version of the "Point Mutation" operator as described in Koza I. Actually, that's not quite true. Koza doesn't have any tree depth restrictions on his mutation operator. This one does -- if the tree gets deeper than the maximum tree depth, then the new subtree is rejected and another one is tried. Similar to how the Crosssover operator is implemented.
Mutated trees are restricted to being maxdepth depth at most. If in tries attemptes, the pipeline cannot come up with a mutated tree within the depth limit, then it simply copies the original individual wholesale with no mutation.
One additional feature: if equal is true, then MutationPipeline will attempt to replace the subtree with a tree of approximately equal size. How this is done exactly, and how close it is, is entirely up to the pipeline's tree builder -- for example, Grow/Full/HalfBuilder don't support this at all, while RandomBranch will replace it with a tree of the same size or "slightly smaller" as described in the algorithm.
Typical Number of Individuals Produced Per produce(...) call
...as many as the child produces
Number of Sources
1
Parameters
base.tries int >= 1 |
(number of times to try finding valid pairs of nodes) |
base.maxdepth int >= 1 |
(maximum valid depth of a crossed-over subtree) |
base.ns classname, inherits and != GPNodeSelector |
(GPNodeSelector for tree) |
base.build.0 classname, inherits and != GPNodeBuilder |
(GPNodeBuilder for new subtree) |
equal bool = true or false (default) |
(do we attempt to replace the subtree with a new one of roughly the same size?) |
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.koza.mutate
Parameter bases
base.ns | nodeselect |
base.build | builder |
Field Summary | |
GPNodeBuilder |
builder
How the pipeline builds a new subtree |
static int |
INDS_PRODUCED
|
GPNodeSelector |
nodeselect
How the pipeline chooses a subtree to mutate |
static int |
NUM_SOURCES
|
static java.lang.String |
P_BUILDER
|
static java.lang.String |
P_EQUALSIZE
|
static java.lang.String |
P_MAXDEPTH
|
static java.lang.String |
P_MUTATION
|
static java.lang.String |
P_NUM_TRIES
|
Fields inherited from class ec.gp.GPBreedingPipeline |
P_NODESELECTOR, P_TREE, TREE_UNFIXED |
Fields inherited from class ec.BreedingPipeline |
DYNAMIC_SOURCES, mybase, P_NUMSOURCES, P_SOURCE, sources, V_SAME |
Fields inherited from class ec.BreedingSource |
CHECKBOUNDARY, DEFAULT_PRODUCED, NO_PROBABILITY, P_PROB, probability, UNUSED |
Constructor Summary | |
MutationPipeline()
|
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. |
Methods inherited from class ec.gp.GPBreedingPipeline |
produces |
Methods inherited from class ec.BreedingPipeline |
individualReplaced, maxChildProduction, minChildProduction, preparePipeline, prepareToProduce, sourcesAreProperForm, typicalIndsProduced |
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_NUM_TRIES
public static final java.lang.String P_MAXDEPTH
public static final java.lang.String P_MUTATION
public static final java.lang.String P_BUILDER
public static final java.lang.String P_EQUALSIZE
public static final int INDS_PRODUCED
public static final int NUM_SOURCES
public GPNodeSelector nodeselect
public GPNodeBuilder builder
Constructor Detail |
public MutationPipeline()
Method Detail |
public Parameter defaultBase()
Prototype
public int numSources()
BreedingPipeline
numSources
in class BreedingPipeline
public java.lang.Object protoClone() throws java.lang.CloneNotSupportedException
Prototype
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.
protoClone
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 class BreedingPipeline
ec.BreedingSource
Prototype.setup(EvolutionState,Parameter)
public int produce(int min, int max, int start, int subpopulation, Individual[] inds, EvolutionState state, int thread) throws java.lang.CloneNotSupportedException
BreedingSource
produce
in class BreedingSource
|
|||||||||
PREV CLASS NEXT CLASS | FRAMES NO FRAMES | ||||||||
SUMMARY: INNER | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |