Documentation of 'ec.vector.FloatVectorSpecies' Java class
FloatVectorSpecies
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ec.vector

Class FloatVectorSpecies

  • All Implemented Interfaces:
    Prototype, Setup, java.io.Serializable, java.lang.Cloneable

    public class FloatVectorSpecies
extends VectorSpecies
    FloatVectorSpecies is a subclass of VectorSpecies with special constraints for floating-point vectors, namely FloatVectorIndividual and DoubleVectorIndividual.

    FloatVectorSpecies can specify a number of parameters globally, per-segment, and per-gene. See VectorSpecies for information on how to this works.

    FloatVectorSpecies defines a minimum and maximum gene value. These values are used during initialization and, depending on whether mutation-bounded is true, also during various mutation algorithms to guarantee that the gene value will not exceed these minimum and maximum bounds.

    FloatVectorSpecies provides support for five ways of mutating a gene.

    • reset Replacing the gene's value with a value uniformly drawn from the gene's range (the default behavior).
    • gaussPerturbing the gene's value with gaussian noise; if the gene-by-gene range is used, than the standard deviation is scaled to reflect each gene's range. If the gaussian mutation's standard deviation is too large for the range, than there's a large probability the mutated value will land outside range. We will try again a number of times (100) before giving up and using the previous mutation method.
    • polynomial Perturbing the gene's value with noise chosen from a polynomial distribution, similar to the gaussian distribution. The polynomial distribution was popularized by Kalyanmoy Deb and is found in many of his publications (see http://www.iitk.ac.in/kangal/deb.shtml). The polynomial distribution has two options. First, there is the index. This variable defines the shape of the distribution and is in some sense the equivalent of the standard deviation in the gaussian distribution. The index is an integer. If it is zero, the polynomial distribution is simply the uniform distribution from [1,-1]. If it is 1, the polynomial distribution is basically a triangular distribution from [1,-1] peaking at 0. If it is 2, the polynomial distribution follows a squared function, again peaking at 0. Larger values result in even more peaking and narrowness. The default values used in nearly all of the NSGA-II and Deb work is 20. Second, there is whether or not the value is intended for bounded genes. The default polynomial distribution is used when we assume the gene can take on literally any value, even beyond the min and max values. For genes which are restricted to be between min and max, there is an alternative version of the polynomial distribution, used by Deb's team but not discussed much in the literature, desiged for that situation. We assume boundedness by default, and have found it to be somewhat better for NSGA-II and SPEA2 problems. For a description of this alternative version, see "A Niched-Penalty Approach for Constraint Handling in Genetic Algorithms" by Kalyanmoy Deb and Samir Agrawal. Deb's default implementation bounds the result to min or max; instead ECJ's implementation of the polynomial distribution retries until it finds a legal value. This will be just fine for ranges like [0,1], but for smaller ranges you may be waiting a long time.
    • integer-reset Replacing the gene's value with a value uniformly drawn from the gene's range but restricted to only integers.
    • integer-random-walk Replacing the gene's value by performing a random walk starting at the gene value. The random walk either adds 1 or subtracts 1 (chosen at random), then does a coin-flip to see whether to continue the random walk. When the coin-flip finally comes up false, the gene value is set to the current random walk position.

    FloatVectorSpecies provides support for two ways of initializing a gene. The initialization procedure is determined by the choice of mutation procedure as described above. If the mutation is floating-point (reset, gauss, polynomial), then initialization will be done by resetting the gene to uniformly chosen floating-point value between the minimum and maximum legal gene values, inclusive. If the mutation is integer (integer-reset, integer-random-walk), then initialization will be done by performing the same kind of reset, but restricting values to integers only.

    Parameters

    double (default=0.0)double >= base.min-gene
    base.min-gene   or
    base.segment.segment-number.min-gene   or
    base.min-gene.gene-number
    0.0 <= double <= 1.0     		(probability that a gene will get mutated over default mutation)
    (the minimum gene value)
     
    base.max-gene   or
    base.segment.segment-number.max-gene   or
    base.max-gene.gene-number
    0.0 <= double <= 1.0     		(probability that a gene will get mutated over default mutation)
    (the maximum gene value)
     
    base.mutation-type   or
    base.segment.segment-number.mutation-type   or
    base.mutation-prob.gene-number
    reset, gauss, polynomial, integer-reset, or integer-random-walk (default=reset)    		 (the mutation type)
     
    base.mutation-stdev   or
    base.segment.segment-number.mutation-stdev   or
    base.mutation-stdev.gene-number
    double ≥ 0    		 (the standard deviation or the gauss perturbation)
    base.distribution-index   or
    base.segment.segment-number.distribution-index   or
    base.distribution-index.gene-number
    int ≥ 0    		 (the mutation distribution index for the polynomial mutation distribution)
     
    base.alternative-polynomial-version   or
    base.segment.segment-number.alternative-polynomial-version   or
    base.alternative-polynomial-version.gene-number
    boolean (default=true)    		 (whether to use the "bounded" variation of the polynomial mutation or the standard ("unbounded") version)
     
    base.random-walk-probability   or
    base.segment.segment-number.random-walk-probability   or
    base.random-walk-probability.gene-number
    0.0 <= double <= 1.0     		(the probability that a random walk will continue. Random walks go up or down by 1.0 until the coin flip comes up false.)
    base.mutation-bounded   or
    base.segment.segment-number.mutation-bounded   or
    base.mutation-bounded.gene-number
    boolean (default=true)    		 (whether mutation is restricted to only being within the min/max gene values. Does not apply to SimulatedBinaryCrossover (which is always bounded))
      base.out-of-bounds-retries
    int ≥ 0 (default=100)    		 (number of times the gaussian mutation got the gene out of range before we give up and reset the gene's value; 0 means "never give up")
    See Also:
    Serialized Form

    • Field Detail

      • P_MUTATION_DISTRIBUTION_INDEX

        public static final java.lang.String P_MUTATION_DISTRIBUTION_INDEX
        See Also:
        Constant Field Values

      • P_POLYNOMIAL_ALTERNATIVE

        public static final java.lang.String P_POLYNOMIAL_ALTERNATIVE
        See Also:
        Constant Field Values

      • V_RESET_MUTATION

        public static final java.lang.String V_RESET_MUTATION
        See Also:
        Constant Field Values

      • V_GAUSS_MUTATION

        public static final java.lang.String V_GAUSS_MUTATION
        See Also:
        Constant Field Values

      • V_POLYNOMIAL_MUTATION

        public static final java.lang.String V_POLYNOMIAL_MUTATION
        See Also:
        Constant Field Values

      • V_INTEGER_RANDOM_WALK_MUTATION

        public static final java.lang.String V_INTEGER_RANDOM_WALK_MUTATION
        See Also:
        Constant Field Values

      • V_INTEGER_RESET_MUTATION

        public static final java.lang.String V_INTEGER_RESET_MUTATION
        See Also:
        Constant Field Values

      • P_RANDOM_WALK_PROBABILITY

        public static final java.lang.String P_RANDOM_WALK_PROBABILITY
        See Also:
        Constant Field Values

      • P_OUTOFBOUNDS_RETRIES

        public static final java.lang.String P_OUTOFBOUNDS_RETRIES
        See Also:
        Constant Field Values

      • P_MUTATION_BOUNDED

        public static final java.lang.String P_MUTATION_BOUNDED
        See Also:
        Constant Field Values

      • C_INTEGER_RESET_MUTATION

        public static final int C_INTEGER_RESET_MUTATION
        See Also:
        Constant Field Values

      • C_INTEGER_RANDOM_WALK_MUTATION

        public static final int C_INTEGER_RANDOM_WALK_MUTATION
        See Also:
        Constant Field Values

      • outOfBoundsRetries

        public int outOfBoundsRetries
        The number of times Polynomial Mutation or Gaussian Mutation retry for valid numbers until they get one.

      • DEFAULT_OUT_OF_BOUNDS_RETRIES

        public static final int DEFAULT_OUT_OF_BOUNDS_RETRIES
        See Also:
        Constant Field Values

    • Constructor Detail

      • FloatVectorSpecies

        public FloatVectorSpecies()

    • Method Detail

      • outOfRangeRetryLimitReached

        public void outOfRangeRetryLimitReached(EvolutionState state)

      • maxGene

        public double maxGene(int gene)

      • minGene

        public double minGene(int gene)

      • mutationType

        public int mutationType(int gene)

      • gaussMutationStdev

        public double gaussMutationStdev(int gene)

      • mutationIsBounded

        public boolean mutationIsBounded(int gene)

      • mutationDistributionIndex

        public int mutationDistributionIndex(int gene)

      • polynomialIsAlternative

        public boolean polynomialIsAlternative(int gene)

      • randomWalkProbability

        public double randomWalkProbability(int gene)

      • inNumericalTypeRange

        public boolean inNumericalTypeRange(double geneVal)

      • setup

        public void setup(EvolutionState state,
                  Parameter base)
        Description copied from class: Species
        The default version of setup(...) loads requested pipelines and calls setup(...) on them and normalizes their probabilities. If your individual prototype might need to know special things about the species (like parameters stored in it), then when you override this setup method, you'll need to set those parameters BEFORE you call super.setup(...), because the setup(...) code in Species sets up the prototype.
        Specified by:
        setup in interface Prototype
        Specified by:
        setup in interface Setup
        Overrides:
        setup in class VectorSpecies
        See Also:
        Prototype.setup(EvolutionState,Parameter)
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