ec.vector

Class DoubleVectorIndividual

java.lang.Object

ec.Individual

ec.vector.VectorIndividual

ec.vector.DoubleVectorIndividual

All Implemented Interfaces:

Prototype, Setup, Serializable, Cloneable, Comparable

Direct Known Subclasses:

MonaVectorIndividual, Particle

public class DoubleVectorIndividual
extends VectorIndividual

DoubleVectorIndividual is a VectorIndividual whose genome is an array of doubles. Gene values may range from species.mingene(x) to species.maxgene(x), inclusive. The default mutation method randomizes genes to new values in this range, with species.mutationProbability. It can also add gaussian noise to the genes, if so directed in the FloatVectorSpecies. If the gaussian noise pushes the gene out of range, a new noise value is generated.

From ec.Individual:

In addition to serialization for checkpointing, Individuals may read and write themselves to streams in three ways.

• writeIndividual(...,DataOutput)/readIndividual(...,DataInput) This method transmits or receives an individual in binary. It is the most efficient approach to sending individuals over networks, etc. These methods write the evaluated flag and the fitness, then call readGenotype/writeGenotype, which you must implement to write those parts of your Individual special to your functions-- the default versions of readGenotype/writeGenotype throw errors. You don't need to implement them if you don't plan on using read/writeIndividual.
• printIndividual(...,PrintWriter)/readIndividual(...,LineNumberReader) This approach transmits or receives an indivdual in text encoded such that the individual is largely readable by humans but can be read back in 100% by ECJ as well. To do this, these methods will encode numbers using the ec.util.Code class. These methods are mostly used to write out populations to files for inspection, slight modification, then reading back in later on. readIndividual reads in the fitness and the evaluation flag, then calls parseGenotype to read in the remaining individual. You are responsible for implementing parseGenotype: the Code class is there to help you. printIndividual writes out the fitness and evaluation flag, then calls genotypeToString and printlns the resultant string. You are responsible for implementing the genotypeToString method in such a way that parseGenotype can read back in the individual println'd with genotypeToString. The default form of genotypeToString simply calls toString, which you may override instead if you like. The default form of parseGenotype throws an error. You are not required to implement these methods, but without them you will not be able to write individuals to files in a simultaneously computer- and human-readable fashion.
• printIndividualForHumans(...,PrintWriter) This approach prints an individual in a fashion intended for human consumption only. printIndividualForHumans writes out the fitness and evaluation flag, then calls genotypeToStringForHumans and printlns the resultant string. You are responsible for implementing the genotypeToStringForHumans method. The default form of genotypeToStringForHumans simply calls toString, which you may override instead if you like (though note that genotypeToString's default also calls toString). You should handle one of these methods properly to ensure individuals can be printed by ECJ.

In general, the various readers and writers do three things: they tell the Fitness to read/write itself, they read/write the evaluated flag, and they read/write the gene array. If you add instance variables to a VectorIndividual or subclass, you'll need to read/write those variables as well. Default Base
vector.double-vect-ind

Version:

2.0

Author:

Liviu Panait, Sean Luke and Liviu Panait

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
double[]	genome
static double	MAXIMUM_INTEGER_IN_DOUBLE
static String	P_DOUBLEVECTORINDIVIDUAL

Fields inherited from class ec.Individual

evaluated, EVALUATED_PREAMBLE, fitness, P_INDIVIDUAL, species

Constructor Summary

Constructors

Constructor and Description
DoubleVectorIndividual()

Method Summary

Methods

Modifier and Type	Method and Description
void	clamp() Clips each gene value to be within its specified [min,max] range.
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.
void	defaultCrossover(EvolutionState state, int thread, VectorIndividual ind) Destructively crosses over the individual with another in some default manner.
void	defaultMutate(EvolutionState state, int thread) Destructively mutates the individual in some default manner.
double	distanceTo(Individual otherInd) Returns the metric distance to another individual, if such a thing can be measured.
boolean	equals(Object ind) Returns true if I am genetically "equal" to ind.
(package private) void	floatResetMutation(MersenneTwisterFast random, FloatVectorSpecies species, int index)
(package private) void	gaussianMutation(EvolutionState state, MersenneTwisterFast random, FloatVectorSpecies species, int index)
int	genomeLength() Returns the length of the gene array.
String	genotypeToString() Print to a string the genotype of the Individual in a fashion intended to be parsed in again via parseGenotype(...).
String	genotypeToStringForHumans() Print to a string the genotype of the Individual in a fashion readable by humans, and not intended to be parsed in again.
Object	getGenome() Returns the gene array.
int	hashCode() Returns a hashcode for the individual, such that individuals which are equals(...) each other always return the same hash code.
(package private) void	integerRandomWalkMutation(MersenneTwisterFast random, FloatVectorSpecies species, int index)
(package private) void	integerResetMutation(MersenneTwisterFast random, FloatVectorSpecies species, int index)
boolean	isInRange() Returns true if each gene value is within is specified [min,max] range.
void	join(Object[] pieces) Joins the n pieces and sets the genome to their concatenation.
protected void	parseGenotype(EvolutionState state, LineNumberReader reader) This method is used only by the default version of readIndividual(state,reader), and it is intended to be overridden to parse in that part of the individual that was outputted in the genotypeToString() method.
void	polynomialMutate(EvolutionState state, MersenneTwisterFast random, double eta_m, boolean alternativePolynomialVersion, boolean mutationIsBounded) This function is broken out to keep it identical to NSGA-II's mutation.c code.
(package private) void	polynomialMutation(EvolutionState state, MersenneTwisterFast random, FloatVectorSpecies species, int index)
(package private) long	randomValueFromClosedInterval(long min, long max, MersenneTwisterFast random)
void	readGenotype(EvolutionState state, DataInput dataInput) Reads in the genotypic information from a DataInput, erasing the previous genotype of this Individual.
void	reset(EvolutionState state, int thread) Initializes the individual by randomly choosing doubles uniformly from mingene to maxgene.
void	setGenome(Object gen) Sets the gene array.
void	setGenomeLength(int len) Sets the genome length.
void	setup(EvolutionState state, Parameter base) This should be used to set up only those things which you share in common with all other individuals in your species; individual-specific items which make you you should be filled in by Species.newIndividual(...), and modified by breeders.
void	simulatedBinaryCrossover(MersenneTwisterFast random, DoubleVectorIndividual other, double eta_c)
void	split(int[] points, Object[] pieces) Splits the genome into n pieces, according to points, which *must* be sorted.
void	writeGenotype(EvolutionState state, DataOutput dataOutput) Writes the genotypic information to a DataOutput.

Methods inherited from class ec.vector.VectorIndividual

cloneGenes, reset, size

Methods inherited from class ec.Individual

compareTo, merge, printIndividual, printIndividual, printIndividual, printIndividualForHumans, printIndividualForHumans, readIndividual, readIndividual, toString, writeIndividual

Methods inherited from class java.lang.Object

finalize, getClass, notify, notifyAll, wait, wait, wait

Field Detail

P_DOUBLEVECTORINDIVIDUAL

public static final String P_DOUBLEVECTORINDIVIDUAL

See Also:

Constant Field Values

MAXIMUM_INTEGER_IN_DOUBLE

public static final double MAXIMUM_INTEGER_IN_DOUBLE

See Also:

Constant Field Values

genome

public double[] genome

Constructor Detail

DoubleVectorIndividual

public DoubleVectorIndividual()

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(...).

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 Individual

setup

public void setup(EvolutionState state,
         Parameter base)

Description copied from class: Individual

This should be used to set up only those things which you share in common with all other individuals in your species; individual-specific items which make you you should be filled in by Species.newIndividual(...), and modified by breeders.

Specified by:

setup in interface Prototype

Specified by:

setup in interface Setup

Overrides:

setup in class Individual

See Also:

Prototype.setup(EvolutionState,Parameter)

defaultCrossover

public void defaultCrossover(EvolutionState state,
                    int thread,
                    VectorIndividual ind)

Description copied from class: VectorIndividual

Destructively crosses over the individual with another in some default manner. In most implementations provided in ECJ, one-, two-, and any-point crossover is done with a for loop, rather than a possibly more efficient approach like arrayCopy(). The disadvantage is that arrayCopy() takes advantage of a CPU's bulk copying. The advantage is that arrayCopy() would require a scratch array, so you'd be allocing and GCing an array for every crossover. Dunno which is more efficient.

Overrides:

defaultCrossover in class VectorIndividual

split

public void split(int[] points,
         Object[] pieces)

Splits the genome into n pieces, according to points, which *must* be sorted. pieces.length must be 1 + points.length

Overrides:

split in class VectorIndividual

join

public void join(Object[] pieces)

Joins the n pieces and sets the genome to their concatenation.

Overrides:

join in class VectorIndividual

defaultMutate

public void defaultMutate(EvolutionState state,
                 int thread)

Destructively mutates the individual in some default manner. The default form simply randomizes genes to a uniform distribution from the min and max of the gene values. It can also add gaussian noise to the genes, if so directed in the FloatVectorSpecies. If the gaussian noise pushes the gene out of range, a new noise value is generated.

Overrides:

defaultMutate in class VectorIndividual

integerRandomWalkMutation

void integerRandomWalkMutation(MersenneTwisterFast random,
                             FloatVectorSpecies species,
                             int index)

integerResetMutation

void integerResetMutation(MersenneTwisterFast random,
                        FloatVectorSpecies species,
                        int index)

floatResetMutation

void floatResetMutation(MersenneTwisterFast random,
                      FloatVectorSpecies species,
                      int index)

gaussianMutation

void gaussianMutation(EvolutionState state,
                    MersenneTwisterFast random,
                    FloatVectorSpecies species,
                    int index)

polynomialMutation

void polynomialMutation(EvolutionState state,
                      MersenneTwisterFast random,
                      FloatVectorSpecies species,
                      int index)

polynomialMutate

public void polynomialMutate(EvolutionState state,
                    MersenneTwisterFast random,
                    double eta_m,
                    boolean alternativePolynomialVersion,
                    boolean mutationIsBounded)

This function is broken out to keep it identical to NSGA-II's mutation.c code. eta_m is the distribution index.

simulatedBinaryCrossover

public void simulatedBinaryCrossover(MersenneTwisterFast random,
                            DoubleVectorIndividual other,
                            double eta_c)

randomValueFromClosedInterval

long randomValueFromClosedInterval(long min,
                                 long max,
                                 MersenneTwisterFast random)

reset

public void reset(EvolutionState state,
         int thread)

Initializes the individual by randomly choosing doubles uniformly from mingene to maxgene.

Specified by:

reset in class VectorIndividual

hashCode

public int hashCode()

Description copied from class: Individual

Returns a hashcode for the individual, such that individuals which are equals(...) each other always return the same hash code.

Specified by:

hashCode in class Individual

genotypeToStringForHumans

public String genotypeToStringForHumans()

Description copied from class: Individual

Print to a string the genotype of the Individual in a fashion readable by humans, and not intended to be parsed in again. The fitness and evaluated flag should not be included. The default form simply calls toString(), but you'll probably want to override this to something else.

Overrides:

genotypeToStringForHumans in class Individual

genotypeToString

public String genotypeToString()

Description copied from class: Individual

Print to a string the genotype of the Individual in a fashion intended to be parsed in again via parseGenotype(...). The fitness and evaluated flag should not be included. The default form simply calls toString(), which is almost certainly wrong, and you'll probably want to override this to something else.

Overrides:

genotypeToString in class Individual

parseGenotype

protected void parseGenotype(EvolutionState state,
                 LineNumberReader reader)
                      throws IOException

Description copied from class: Individual

This method is used only by the default version of readIndividual(state,reader), and it is intended to be overridden to parse in that part of the individual that was outputted in the genotypeToString() method. The default version of this method exits the program with an "unimplemented" error. You'll want to override this method, or to override readIndividual(...) to not use this method.

Overrides:

parseGenotype in class Individual

Throws:

IOException

equals

public boolean equals(Object ind)

Description copied from class: Individual

Returns true if I am genetically "equal" to ind. This should mostly be interpreted as saying that we are of the same class and that we hold the same data. It should NOT be a pointer comparison.

Specified by:

equals in class Individual

getGenome

public Object getGenome()

Description copied from class: VectorIndividual

Returns the gene array. If you know the type of the array, you can cast it and work on it directly. Otherwise, you can still manipulate it in general, because arrays (like all objects) respond to clone() and can be manipulated with arrayCopy without bothering with their type. This might be useful in creating special generalized crossover operators -- we apologize in advance for the fact that Java doesn't have a template system. :-( The default version returns null.

Overrides:

getGenome in class VectorIndividual

setGenome

public void setGenome(Object gen)

Description copied from class: VectorIndividual

Sets the gene array. See getGenome(). The default version does nothing.

Overrides:

setGenome in class VectorIndividual

genomeLength

public int genomeLength()

Description copied from class: VectorIndividual

Returns the length of the gene array. By default, this method returns 0.

Overrides:

genomeLength in class VectorIndividual

writeGenotype

public void writeGenotype(EvolutionState state,
                 DataOutput dataOutput)
                   throws IOException

Description copied from class: Individual

Writes the genotypic information to a DataOutput. Largely called by writeIndividual(), and nothing else. The default simply throws an error. Various subclasses of Individual override this as appropriate. For example, if your custom individual's genotype consists of an array of integers, you might do this:

 dataOutput.writeInt(integers.length);
 for(int x=0;x

Overrides:

writeGenotype in class Individual

Throws:

IOException

readGenotype

public void readGenotype(EvolutionState state,
                DataInput dataInput)
                  throws IOException

Description copied from class: Individual

Reads in the genotypic information from a DataInput, erasing the previous genotype of this Individual. Largely called by readIndividual(), and nothing else. If you are trying to create an Individual from information read in from a stream or DataInput, see the various newIndividual() methods in Species. The default simply throws an error. Various subclasses of Individual override this as appropriate. For example, if your custom individual's genotype consists of an array of integers, you might do this:

 integers = new int[dataInput.readInt()];
 for(int x=0;x

Overrides:

readGenotype in class Individual

Throws:

IOException

clamp

public void clamp()

Clips each gene value to be within its specified [min,max] range. NaN is presently considered in range but the behavior of this method should be assumed to be unspecified on encountering NaN.

setGenomeLength

public void setGenomeLength(int len)

Description copied from class: VectorIndividual

Sets the genome length. If the length is longer, then it is filled with a default value (likely 0 or false). This may or may not be a valid value -- you will need to set appropriate values here. The default implementation does nothing; but all subclasses in ECJ implement a subset of this.

Overrides:

setGenomeLength in class VectorIndividual

isInRange

public boolean isInRange()

Returns true if each gene value is within is specified [min,max] range. NaN is presently considered in range but the behavior of this method should be assumed to be unspecified on encountering NaN.

distanceTo

public double distanceTo(Individual otherInd)

Description copied from class: Individual

Returns the metric distance to another individual, if such a thing can be measured. Subclassess of Individual should implement this if it exists for their representation. The default implementation here, which isn't very helpful, returns 0 if the individuals are equal and infinity if they are not.

Overrides:

distanceTo in class Individual