ec.gp.breed
Class MutateERCPipeline

java.lang.Object
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  +--ec.BreedingSource
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        +--ec.BreedingPipeline
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              +--ec.gp.GPBreedingPipeline
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                    +--ec.gp.breed.MutateERCPipeline

public class MutateERCPipeline
extends GPBreedingPipeline

MutateERCPipeline works very similarly to the "Gaussian" algorithm described in Kumar Chellapilla, "A Preliminary Investigation into Evolving Modular Programs without Subtree Crossover", GP98.

MutateERCPipeline picks a random node from a random tree in the individual, using its node selector. It then proceeds to "mutate" every ERC (ephemeral random constant) located in the subtree rooted at that node. It does this by calling each ERC's mutateERC() method. The default form of mutateERC() method is to simply call resetNode(), thus randomizing the ERC; you may want to override this default to provide more useful mutations, such as adding gaussian noise.

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-erc

Parameter bases
base.ns
The GPNodeSelector selector

See Also:
Serialized Form

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_MUTATEERC
           
 
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
MutateERCPipeline()
           
 
Method Summary
 Parameter defaultBase()
          Returns the default base for this prototype.
 void mutateERCs(GPNode node, EvolutionState state, int thread)
           
 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

P_MUTATEERC

public static final java.lang.String P_MUTATEERC

INDS_PRODUCED

public static final int INDS_PRODUCED

NUM_SOURCES

public static final int NUM_SOURCES

nodeselect

public GPNodeSelector nodeselect
How the pipeline chooses a subtree to mutate
Constructor Detail

MutateERCPipeline

public MutateERCPipeline()
Method Detail

defaultBase

public Parameter defaultBase()
Description copied from interface: Prototype
Returns the default base for this prototype. This should generally be implemented by building off of the static base() method on the DefaultsForm object for the prototype's package. This should be callable during setup(...).

numSources

public int numSources()
Description copied from class: BreedingPipeline
Returns the number of sources to this pipeline. Called during BreedingPipeline's setup. Be sure to return a value > 0, or DYNAMIC_SOURCES which indicates that setup should check the parameter file for the parameter "num-sources" to make its determination.
Overrides:
numSources in class BreedingPipeline

protoClone

public java.lang.Object protoClone()
                            throws java.lang.CloneNotSupportedException
Description copied from interface: Prototype
Creates a new individual cloned from a prototype, and suitable to begin use in its own evolutionary context.

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.

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.

Overrides:
protoClone in class BreedingPipeline

setup

public void setup(EvolutionState state,
                  Parameter base)
Description copied from class: BreedingSource
Sets up the BreedingPipeline. You can use state.output.error here because the top-level caller promises to call exitIfErrors() after calling setup. Note that probability might get modified again by an external source if it doesn't normalize right.

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}.

Overrides:
setup in class BreedingPipeline
Tags copied from class: BreedingSource
See Also:
Prototype.setup(EvolutionState,Parameter)

typicalIndsProduced

public int typicalIndsProduced()
Returns 1
Overrides:
typicalIndsProduced in class BreedingSource

mutateERCs

public final void mutateERCs(GPNode node,
                             EvolutionState state,
                             int thread)

produce

public int produce(int min,
                   int max,
                   int start,
                   int subpopulation,
                   Individual[] inds,
                   EvolutionState state,
                   int thread)
            throws java.lang.CloneNotSupportedException
Description copied from class: BreedingSource
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. max must be >= min, and min must be >= 1. For example, crossover might typically produce two individuals, tournament selection might typically produce a single individual, etc.
Overrides:
produce in class BreedingSource