Package ec.vector.breed

Class ListCrossoverPipeline

java.lang.Object
ec.BreedingSource
ec.BreedingPipeline
ec.vector.breed.ListCrossoverPipeline
All Implemented Interfaces:
Prototype, Setup, SteadyStateBSourceForm, RandomChoiceChooserD, Serializable, Cloneable
public class ListCrossoverPipeline extends BreedingPipeline
ListCrossoverPipeline is a crossover pipeline for vector individuals whose length may be lengthened or shortened. There are two crossover options available: one-point and two-point. One-point crossover picks a crossover point for each of the vectors (the crossover point can be different), and then does one-point crossover using those points. Two-point crossover picks TWO crossover points for each of the vectors (again, the points can be different among the vectors), and swaps the middle regions between the respective crossover points.

ListCrossoverPipeline will try tries times to meet certain constraints: first, the resulting children must be no smaller than min-child-size. Second, the amount of material removed from a parent must be no less than mix-crossover-percent and no more than max-crossover-percent.

If toss is true, then only one child is generated, else at most two are generated.

Typical Number of Individuals Produced Per produce(...) call
2 * minimum typical number of individuals produced by each source, unless toss is set, in which case it's simply the minimum typical number.

Number of Sources
2

Parameters

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.tries
int >= 1		 (number of times to try finding valid crossover points)
base.min-child-size
int >= 0 (default)		 (the minimum allowed size of a child)
base.min-crossover-percent
0 (default) <= double <= 1		 (the minimum percentage of an individual that may be removed during crossover)
base.max-crossover-percent
0 <= double <= 1 (default)		 (the maximum percentage of an individual that may be removed during crossover)

Default Base
vector.list-xover

See Also:

  • Field Details

  • Constructor Details

    • ListCrossoverPipeline

      public ListCrossoverPipeline()

  • 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()
      Description copied from class: BreedingPipeline
      Returns the "typical" number of individuals produced -- by default this is the minimum typical number of individuals produced by any children sources of the pipeline. If you'd prefer something different, override this method.
      Overrides:
      typicalIndsProduced in class BreedingPipeline

    • 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

    • computeValidationData

      public Object computeValidationData(EvolutionState state, ArrayList<Individual> parents, int thread)
      A hook called by ListCrossoverPipeline to allow subclasses to prepare for additional validation testing. Primarily used by GECrossoverPipeline.

    • isValidated

      public boolean isValidated(int[][] split, Object validationData)
      A hook called by ListCrossoverPipeline to allow subclasses to further validate children crossover points. Primarily used by GECrossoverPipeline.