ec.gp.breed

Class MutateERCPipeline

java.lang.Object

ec.BreedingSource

ec.BreedingPipeline

ec.gp.GPBreedingPipeline

ec.gp.breed.MutateERCPipeline

All Implemented Interfaces:

Prototype, Setup, SteadyStateBSourceForm, RandomChoiceChooserD, Serializable, Cloneable

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
...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-erc

Parameter bases

base.ns

The GPNodeSelector selector

Version:

1.0

Author:

Sean Luke

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
GPNodeSelector	nodeselect How the pipeline chooses a subtree to mutate
static int	NUM_SOURCES
static String	P_MUTATEERC
private static long	serialVersionUID
(package private) int	tree Is our 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, likelihood, mybase, P_LIKELIHOOD, P_NUMSOURCES, P_SOURCE, sources, V_SAME

Fields inherited from class ec.BreedingSource

NO_PROBABILITY, P_PROB, probability

Constructor Summary

Constructors

Constructor and Description
MutateERCPipeline()

Method Summary

Methods

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.
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.
void	setup(EvolutionState state, Parameter base) Sets up the BreedingPipeline.

Methods inherited from class ec.gp.GPBreedingPipeline

produces

Methods inherited from class ec.BreedingPipeline

finishProducing, individualReplaced, maxChildProduction, minChildProduction, preparePipeline, prepareToProduce, reproduce, sourcesAreProperForm, typicalIndsProduced

Methods inherited from class ec.BreedingSource

getProbability, pickRandom, setProbability, setupProbabilities

Methods inherited from class java.lang.Object

equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

serialVersionUID

private static final long serialVersionUID

See Also:

Constant Field Values

P_MUTATEERC

public static final String P_MUTATEERC

See Also:

Constant Field Values

NUM_SOURCES

public static final int NUM_SOURCES

See Also:

Constant Field Values

nodeselect

public GPNodeSelector nodeselect

How the pipeline chooses a subtree to mutate

tree

int tree

Is our tree fixed? If not, this is -1

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.

Specified by:

numSources in class BreedingPipeline

clone

public Object clone()

Description copied from interface: Prototype

Creates a new individual cloned from a prototype, and suitable to begin use in its own evolutionary context.

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:

• If you hold objects which are shared with other instances, don't clone them.
• If you hold objects which must be unique, clone them.
• If you hold objects which were given to you as a gesture of kindness, and aren't owned by you, you probably shouldn't clone them.
• DON'T attempt to clone: Singletons, Cliques, or Groups.
• Arrays are not cloned automatically; you may need to clone an array if you're not sharing it with other instances. Arrays have the nice feature of being copyable by calling clone() on them.

Implementations.

• If no ancestor of yours implements clone(), and you have no need to do clone deeply, and you are abstract, then you should not declare clone().
• If no ancestor of yours implements clone(), and you have no need to do clone deeply, and you are not abstract, then you should implement it as follows:

   public Object clone() 
       {
       try
           { 
           return super.clone();
           }
       catch ((CloneNotSupportedException e)
           { throw new InternalError(); } // never happens
       }
          

• If no ancestor of yours implements clone(), but you need to deep-clone some things, then you should implement it as follows:

   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;
       } 
          

• If an ancestor has implemented clone(), and you also need to deep clone some things, then you should implement it as follows:

   public Object clone() 
       { 
       MyObject myobj = (MyObject) (super.clone());
  
       // put your deep-cloning code here...
  
       return myobj;
       } 
          

Specified by:

clone in interface Prototype

Overrides:

clone 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}.

Specified by:

setup in interface Prototype

Specified by:

setup in interface Setup

Overrides:

setup in class BreedingPipeline

See Also:

Prototype.setup(EvolutionState,Parameter)

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)

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.

Specified by:

produce in class BreedingSource