Package ec.vector.breed

Class MultipleVectorCrossoverPipeline

java.lang.Object
ec.BreedingSource
ec.BreedingPipeline
ec.vector.breed.MultipleVectorCrossoverPipeline
All Implemented Interfaces:
Prototype, Setup, SteadyStateBSourceForm, RandomChoiceChooserD, Serializable, Cloneable
public class MultipleVectorCrossoverPipeline extends BreedingPipeline
MultipleVectorCrossoverPipeline is a BreedingPipeline which implements a uniform (any point) crossover between multiple vectors. It is intended to be used with three or more vectors. It takes n parent individuals and returns n crossed over individuals. The number of parents and consequently children is specified by the number of sources parameter.

The standard vector crossover probability is used for this crossover type.
Note : It is necessary to set the crossover-type parameter to 'any' in order to use this pipeline.

Typical Number of Individuals Produced Per produce(...) call
number of parents

Number of Sources
variable (generally 3 or more)

Default Base
vector.multixover

See Also:

  • Field Details

  • Constructor Details

    • MultipleVectorCrossoverPipeline

      public MultipleVectorCrossoverPipeline()

  • 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()
      Returns the number of parents
      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 the minimum number of children that are produced per crossover
      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