Documentation of 'ec.gp.koza.MutationPipeline' Java class
MutationPipeline
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ec.gp.koza

Class MutationPipeline

  • All Implemented Interfaces:
    Prototype, Setup, SteadyStateBSourceForm, RandomChoiceChooserD, java.io.Serializable, java.lang.Cloneable

    public class MutationPipeline
extends GPBreedingPipeline
    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 and at most maxsize number of nodes. 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.maxsize
    int >= 1    		 (maximum valid size, in nodes, 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
    See Also:
    Serialized Form

    • Constructor Detail

      • MutationPipeline

        public MutationPipeline()

    • 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 java.lang.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)

      • verifyPoints

        public boolean verifyPoints(GPNode inner1,
                            GPNode inner2)
        Returns true if inner1 can feasibly be swapped into inner2's position

      • 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
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