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:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
static String	P_CROSSOVER default base
(package private) VectorIndividual[]	parents Temporary holding place for parents

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
MultipleVectorCrossoverPipeline()

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.
int	multipleBitVectorCrossover(int min, int max, int start, int subpopulation, Individual[] inds, EvolutionState state, int thread) Crosses over the Bit Vector Individuals using a uniform crossover method.
int	multipleByteVectorCrossover(int min, int max, int start, int subpopulation, Individual[] inds, EvolutionState state, int thread) Crosses over the Byte Vector Individuals using a uniform crossover method.
int	multipleDoubleVectorCrossover(int min, int max, int start, int subpopulation, Individual[] inds, EvolutionState state, int thread) Crosses over the Double Vector Individuals using a uniform crossover method.
int	multipleFloatVectorCrossover(int min, int max, int start, int subpopulation, Individual[] inds, EvolutionState state, int thread) Crosses over the Float Vector Individuals using a uniform crossover method.
int	multipleGeneVectorCrossover(int min, int max, int start, int subpopulation, Individual[] inds, EvolutionState state, int thread) Crosses over the Gene Vector Individuals using a uniform crossover method.
int	multipleIntegerVectorCrossover(int min, int max, int start, int subpopulation, Individual[] inds, EvolutionState state, int thread) Crosses over the Integer Vector Individuals using a uniform crossover method.
int	multipleLongVectorCrossover(int min, int max, int start, int subpopulation, Individual[] inds, EvolutionState state, int thread) Crosses over the Long Vector Individuals using a uniform crossover method.
int	multipleShortVectorCrossover(int min, int max, int start, int subpopulation, Individual[] inds, EvolutionState state, int thread) Crosses over the Short Vector Individuals using a uniform crossover method.
int	numSources() Returns the number of parents
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.
int	typicalIndsProduced() Returns the minimum number of children that are produced per crossover

Methods inherited from class ec.BreedingPipeline

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

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

P_CROSSOVER

public static final String P_CROSSOVER

default base

See Also:

Constant Field Values

parents

VectorIndividual[] parents

Temporary holding place for parents

Constructor Detail

MultipleVectorCrossoverPipeline

public MultipleVectorCrossoverPipeline()

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()

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 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)

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 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

multipleBitVectorCrossover

public int multipleBitVectorCrossover(int min,
                             int max,
                             int start,
                             int subpopulation,
                             Individual[] inds,
                             EvolutionState state,
                             int thread)

Crosses over the Bit Vector Individuals using a uniform crossover method. There is no need to call this method separately; produce(...) calls it whenever necessary by default.

multipleByteVectorCrossover

public int multipleByteVectorCrossover(int min,
                              int max,
                              int start,
                              int subpopulation,
                              Individual[] inds,
                              EvolutionState state,
                              int thread)

Crosses over the Byte Vector Individuals using a uniform crossover method. There is no need to call this method separately; produce(...) calls it whenever necessary by default.

multipleDoubleVectorCrossover

public int multipleDoubleVectorCrossover(int min,
                                int max,
                                int start,
                                int subpopulation,
                                Individual[] inds,
                                EvolutionState state,
                                int thread)

Crosses over the Double Vector Individuals using a uniform crossover method. There is no need to call this method separately; produce(...) calls it whenever necessary by default.

multipleFloatVectorCrossover

public int multipleFloatVectorCrossover(int min,
                               int max,
                               int start,
                               int subpopulation,
                               Individual[] inds,
                               EvolutionState state,
                               int thread)

Crosses over the Float Vector Individuals using a uniform crossover method. There is no need to call this method separately; produce(...) calls it whenever necessary by default.

multipleGeneVectorCrossover

public int multipleGeneVectorCrossover(int min,
                              int max,
                              int start,
                              int subpopulation,
                              Individual[] inds,
                              EvolutionState state,
                              int thread)

Crosses over the Gene Vector Individuals using a uniform crossover method. There is no need to call this method separately; produce(...) calls it whenever necessary by default.

multipleIntegerVectorCrossover

public int multipleIntegerVectorCrossover(int min,
                                 int max,
                                 int start,
                                 int subpopulation,
                                 Individual[] inds,
                                 EvolutionState state,
                                 int thread)

Crosses over the Integer Vector Individuals using a uniform crossover method. There is no need to call this method separately; produce(...) calls it whenever necessary by default.

multipleLongVectorCrossover

public int multipleLongVectorCrossover(int min,
                              int max,
                              int start,
                              int subpopulation,
                              Individual[] inds,
                              EvolutionState state,
                              int thread)

Crosses over the Long Vector Individuals using a uniform crossover method. There is no need to call this method separately; produce(...) calls it whenever necessary by default.

multipleShortVectorCrossover

public int multipleShortVectorCrossover(int min,
                               int max,
                               int start,
                               int subpopulation,
                               Individual[] inds,
                               EvolutionState state,
                               int thread)

Crosses over the Short Vector Individuals using a uniform crossover method. There is no need to call this method separately; produce(...) calls it whenever necessary by default.