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java.lang.Object | +--ec.BreedingSource | +--ec.BreedingPipeline | +--ec.gp.GPBreedingPipeline | +--ec.gp.koza.CrossoverPipeline
CrossoverPipeline is a GPBreedingPipeline which performs a strongly-typed version of Koza-style "Subtree Crossover". Two individuals are selected, then a single tree is chosen in each such that the two trees have the same GPTreeConstraints. Then a random node is chosen in each tree such that the two nodes have the same return type. If by swapping subtrees at these nodes the two trees will not violate maximum depth constraints, then the trees perform the swap, otherwise, they repeat the hunt for random nodes.
The pipeline tries at most tries times to a pair of random nodes BOTH with valid swap constraints. If it cannot find any such pairs after tries times, it uses the pair of its last attempt. If either of the nodes in the pair is valid, that node gets substituted with the other node. Otherwise an individual invalid node isn't changed at all (it's "reproduced").
Compatibility with constraints. Since Koza-I/II only tries 1 time, and then follows this policy, this is compatible with Koza. lil-gp either tries 1 time, or tries forever. Either way, this is compatible with lil-gp. My hacked lil-gp kernel either tries 1 time, n times, or forever. This is compatible as well.
This pipeline typically produces up to 2 new individuals (the two newly- swapped individuals) per produce(...) call. If the system only needs a single individual, the pipeline will throw one of the new individuals away. The user can also have the pipeline always throw away the second new individual instead of adding it to the population. In this case, the pipeline will only typically produce 1 new individual per produce(...) call.
Typical Number of Individuals Produced Per produce(...) call
2 * minimum typical number of individuals produced by each source, unless tossSecondParent
is set, in which case it's simply the minimum typical number.
Number of Sources
2
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.tree.0 0 < int < (num trees in individuals), if exists |
(first tree for the crossover; if parameter doesn't exist, tree is picked at random) |
base.tree.1 0 < int < (num trees in individuals), if exists |
(second tree for the crossover; if parameter doesn't exist, tree is picked at random. This tree must have the same GPTreeConstraints as tree.0, if tree.0 is defined.) |
base.ns.n classname, inherits and != GPNodeSelector, or String same |
(GPNodeSelector for parent n (n is 0 or 1) If, for ns.1 the value is same, then ns.1 a copy of whatever ns.0 is. Note that the default version has no n) |
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?) |
Default Base
gp.koza.xover
Parameter bases
base.ns.n | nodeselectn (n is 0 or 1) |
Field Summary | |
static int |
INDS_PRODUCED
|
int |
maxDepth
The deepest tree the pipeline is allowed to form. |
GPNodeSelector |
nodeselect1
How the pipeline selects a node from individual 1 |
GPNodeSelector |
nodeselect2
How the pipeline selects a node from individual 2 |
static int |
NUM_SOURCES
|
int |
numTries
How many times the pipeline attempts to pick nodes until it gives up. |
static java.lang.String |
P_CROSSOVER
|
static java.lang.String |
P_MAXDEPTH
|
static java.lang.String |
P_NUM_TRIES
|
static java.lang.String |
P_TOSS
|
GPIndividual[] |
parents
Temporary holding place for parents |
boolean |
tossSecondParent
Should the pipeline discard the second parent after crossing over? |
int |
tree1
Is the first tree fixed? If not, this is -1 |
int |
tree2
Is the second tree fixed? If not, this is -1 |
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 | |
CrossoverPipeline()
|
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 2 * minimum number of typical individuals produced by any sources, else 1* minimum number if tossSecondParent is true. |
boolean |
verifyPoints(GPNode inner1,
GPNode inner2)
Returns true if inner1 can feasibly be swapped into inner2's position. |
Methods inherited from class ec.gp.GPBreedingPipeline |
produces |
Methods inherited from class ec.BreedingPipeline |
individualReplaced, maxChildProduction, minChildProduction, preparePipeline, prepareToProduce, sourcesAreProperForm |
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_CROSSOVER
public static final java.lang.String P_TOSS
public static final int INDS_PRODUCED
public static final int NUM_SOURCES
public GPNodeSelector nodeselect1
public GPNodeSelector nodeselect2
public int tree1
public int tree2
public int numTries
public int maxDepth
public boolean tossSecondParent
public GPIndividual[] parents
Constructor Detail |
public CrossoverPipeline()
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 typicalIndsProduced()
typicalIndsProduced
in class BreedingPipeline
public final boolean verifyPoints(GPNode inner1, GPNode inner2)
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
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