Class SigmaScaling

java.lang.Object
org.cicirello.search.evo.SigmaScaling
All Implemented Interfaces:
Splittable<SelectionOperator>, SelectionOperator

public final class SigmaScaling extends Object implements SelectionOperator

Implements sigma scaling by wrapping your chosen selection operator. The SigmaScaling instance applies sigma scaling to the fitnesses of the population, passing the scaled fitnesses to the true selection operator.

The fitness, fi, of population member i is transformed as follows: f'i = fi - ( f̅ - c * σ ), where c is a parameter usually between 1 and 3, inclusive. σ is the standard deviation of the fitnesses of the population, and f̅ is average fitness. For any members of the population for which this transformation produces a fitness less than 0.001, it is reset to 0.001.

The intended use-case is to use in combination with a fitness weighted selection operator, such as FitnessProportionalSelection, StochasticUniversalSampling, BiasedFitnessProportionalSelection, or BiasedStochasticUniversalSampling. Here is an example of how to instantiate an instance of a selection operator using SigmaScaling:


 SelectionOperator selection = new SigmaScaling(new FitnessProportionalSelection(), 2);
 
  • Field Summary

    Fields
    Modifier and Type
    Field
    Description
    static final double
    The minimum scaled fitness, such that any fitnesses that lead to scaled fitness less than this value, are reset to this value.
  • Constructor Summary

    Constructors
    Constructor
    Description
    Constructs a new SigmaScaling object, to transform fitness values via sigma scaling prior to selection by a wrapped selection operator.
    SigmaScaling(SelectionOperator selection, double c)
    Constructs a new SigmaScaling object, to transform fitness values via sigma scaling prior to selection by a wrapped selection operator.
  • Method Summary

    Modifier and Type
    Method
    Description
    void
    init(int generations)
    Perform any initialization necessary for the selection operator at the start of the run of the evolutionary algorithm.
    void
    select(PopulationFitnessVector.Double fitnesses, int[] selected)
    Selects a set of members of the population based on fitness.
    void
    select(PopulationFitnessVector.Integer fitnesses, int[] selected)
    Selects a set of members of the population based on fitness.
    Generates a functionally identical copy of this object, for use in multithreaded implementations of search algorithms.

    Methods inherited from class java.lang.Object

    clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
  • Field Details

    • MIN_SCALED_FITNESS

      public static final double MIN_SCALED_FITNESS
      The minimum scaled fitness, such that any fitnesses that lead to scaled fitness less than this value, are reset to this value.
      See Also:
  • Constructor Details

    • SigmaScaling

      public SigmaScaling(SelectionOperator selection)
      Constructs a new SigmaScaling object, to transform fitness values via sigma scaling prior to selection by a wrapped selection operator. Uses a default c = 2.0.
      Parameters:
      selection - The selection operator.
    • SigmaScaling

      public SigmaScaling(SelectionOperator selection, double c)
      Constructs a new SigmaScaling object, to transform fitness values via sigma scaling prior to selection by a wrapped selection operator.
      Parameters:
      selection - The selection operator.
      c - See class documentation for details of this parameter.
  • Method Details

    • select

      public void select(PopulationFitnessVector.Integer fitnesses, int[] selected)
      Description copied from interface: SelectionOperator
      Selects a set of members of the population based on fitness. Implementations should ensure that the array of indexes of population members is in a random order. For some selection operators, this required behavior is met by definition (e.g., the common fitness proportionate selection will have this behavior as is). But other selection operators may require randomizing the array of indexes after selection. For example, the obvious implementation of stochastic universal sampling will likely have all copies of an individual population member ordered together, and thus will require a shuffling of the array before returning.
      Specified by:
      select in interface SelectionOperator
      Parameters:
      fitnesses - A vector of fitnesses of the members of the population.
      selected - An array for the result. The selection operator should select selected.length members of the population based on fitnesses, populating selected with the indexes of the chosen members. Note that selected.length may be different than the fitnesses.size().
    • select

      public void select(PopulationFitnessVector.Double fitnesses, int[] selected)
      Description copied from interface: SelectionOperator
      Selects a set of members of the population based on fitness. Implementations should ensure that the array of indexes of population members is in a random order. For some selection operators, this required behavior is met by definition (e.g., the common fitness proportionate selection will have this behavior as is). But other selection operators may require randomizing the array of indexes after selection. For example, the obvious implementation of stochastic universal sampling will likely have all copies of an individual population member ordered together, and thus will require a shuffling of the array before returning.
      Specified by:
      select in interface SelectionOperator
      Parameters:
      fitnesses - A vector of fitnesses of the members of the population.
      selected - An array for the result. The selection operator should select selected.length members of the population based on fitnesses, populating selected with the indexes of the chosen members. Note that selected.length may be different than the fitnesses.size().
    • init

      public void init(int generations)
      Description copied from interface: SelectionOperator
      Perform any initialization necessary for the selection operator at the start of the run of the evolutionary algorithm. This method is called by the evolutionary algorithm at the start of a run (i.e., whenever an EA's optimize or reoptimize methods are called. The default implementation of this method does nothing, which is appropriate for most selection operators since the behavior of most standard selection operators is doesn't change during runs. However, some selection operators may adjust behavior during the run, such as Boltzmann selection which adjusts a temperature parameter. The init method enables reinitializing such parameters at the start of runs.
      Specified by:
      init in interface SelectionOperator
      Parameters:
      generations - The number of generations for the run of the evolutionary algorithm about to commence.
    • split

      public SigmaScaling split()
      Description copied from interface: Splittable
      Generates a functionally identical copy of this object, for use in multithreaded implementations of search algorithms. The state of the object that is returned may or may not be identical to that of the original. Thus, this is a distinct concept from the functionality of the Copyable interface. Classes that implement this interface must ensure that the object returned performs the same functionality, and that it does not share any state data that would be either unsafe or inefficient for concurrent access by multiple threads. The split method is allowed to simply return the this reference, provided that it is both safe and efficient for multiple threads to share a single copy of the Splittable object. The intention is to provide a multithreaded search with the capability to provide spawned threads with their own distinct search operators. Such multithreaded algorithms can call the split method for each thread it spawns to generate a functionally identical copy of the operator, but with independent state.
      Specified by:
      split in interface Splittable<SelectionOperator>
      Returns:
      A functionally identical copy of the object, or a reference to this if it is both safe and efficient for multiple threads to share a single instance of this Splittable object.