Package ec.gp.breed

Class SizeFairCrossoverPipeline

java.lang.Object
ec.BreedingSource
ec.BreedingPipeline
ec.gp.GPBreedingPipeline
ec.gp.breed.SizeFairCrossoverPipeline
All Implemented Interfaces:
Prototype, Setup, SteadyStateBSourceForm, RandomChoiceChooserD, Serializable, Cloneable
public class SizeFairCrossoverPipeline extends GPBreedingPipeline
SizeFairCrossover works similarly to one written in the paper "Size Fair and Homologous Tree Genetic Programming Crossovers" by Langdon (1998).

SizeFairCrossover tries tries times to find a tree that has at least one fair size node based on size fair or homologous implementation. If it cannot find a valid tree in tries times, it gives up and simply copies the individual.

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?)
base.homologous
bool = true or false (default)/td> 		(Is the implementation homologous (as opposed to size-fair)?)

Default Base
gp.breed.size-fair

Parameter bases

base.ns.n
nodeselectn (n is 0 or 1)
See Also:

  • Field Details

    • P_NUM_TRIES

      public static final String P_NUM_TRIES
      See Also:

    • P_MAXDEPTH

      public static final String P_MAXDEPTH
      See Also:

    • P_SIZEFAIR

      public static final String P_SIZEFAIR
      See Also:

    • P_TOSS

      public static final String P_TOSS
      See Also:

    • P_HOMOLOGOUS

      public static final String P_HOMOLOGOUS
      See Also:

    • INDS_PRODUCED

      public static final int INDS_PRODUCED
      See Also:

    • NUM_SOURCES

      public static final int NUM_SOURCES
      See Also:

    • KEY_PARENTS

      public static final String KEY_PARENTS
      See Also:

    • nodeselect1

      public GPNodeSelector nodeselect1
      How the pipeline selects a node from individual 1

    • nodeselect2

      public GPNodeSelector nodeselect2
      How the pipeline selects a node from individual 2

    • tree1

      public int tree1
      Is the first tree fixed? If not, this is -1

    • tree2

      public int tree2
      Is the second tree fixed? If not, this is -1

    • numTries

      public int numTries
      How many times the pipeline attempts to pick nodes until it gives up.

    • maxDepth

      public int maxDepth
      The deepest tree the pipeline is allowed to form. Single terminal trees are depth 1.

    • tossSecondParent

      public boolean tossSecondParent
      Should the pipeline discard the second parent after crossing over?

    • parents

      public ArrayList<Individual> parents
      Temporary holding place for parents

    • homologous

      public boolean homologous

  • Constructor Details

    • SizeFairCrossoverPipeline

      public SizeFairCrossoverPipeline()

  • Method Details

    • 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 Populations, or Subpopulation.
      • 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:

    • typicalIndsProduced

      public int typicalIndsProduced()
      Returns 2 * minimum number of typical individuals produced by any sources, else 1* minimum number if tossSecondParent is true.
      Overrides:
      typicalIndsProduced in class BreedingPipeline

    • verifyPoints

      public boolean verifyPoints(GPInitializer initializer, 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 subpopulation, ArrayList<Individual> inds, EvolutionState state, int thread, HashMap<String,Object> misc)
      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

    • findFairSizeNode

      protected GPNode findFairSizeNode(ArrayList nodeToSubtrees, HashMap sizeToNodes, GPNode parent1SelectedNode, GPTree tree2, EvolutionState state, int thread)
      This method finds a node using the logic given in the langdon paper.
      Parameters:
      nodeToSubtrees - For Tree of Parent2 all precomputed stats about depth,subtrees etc
      sizeToNodes - Quick lookup for LinkedList of size to Nodes
      parent1SelectedNode - Node selected in parent1
      tree2 - Tree of parent2
      state - Evolution State passed for getting access to Random Object of MersenneTwiser
      thread - thread number

    • traverseTreeForDepth

      public void traverseTreeForDepth(GPNode node, ArrayList nodeToDepth, HashMap sizeToNodes)
      Recursively travel the tree so that depth and subtree below are computed only once and can be reused later.
      Parameters:
      node -
      nodeToDepth -