Module org.cicirello.chips_n_salsa
Package org.cicirello.search.evo

Class BiasedStochasticUniversalSampling

java.lang.Object
org.cicirello.search.evo.StochasticUniversalSampling
org.cicirello.search.evo.BiasedStochasticUniversalSampling
All Implemented Interfaces:
Splittable<SelectionOperator>, SelectionOperator
Direct Known Subclasses:
BoltzmannStochasticUniversalSampling
public class BiasedStochasticUniversalSampling extends StochasticUniversalSampling
This class implements a variation of Stochastic Universal Sampling (SUS) that we call Biased Stochastic Universal Sampling (Biased SUS), which integrates the use of a bias function with SUS to enable transforming fitness values prior to the stochastic selection decisions. In this Biased SUS, a member of the population is chosen randomly with probability proportional to a bias function of its fitness relative to the total of such biased fitness of the population. For example, if the fitness of population member i is fi, then the probability of selecting population member i is: bias(fi) / ∑j bias(fj), for j ∈ { 1, 2, ..., N }, where N is the population size, and bias is a bias function.

As an example bias function, consider: bias(x) = x2, which would square each fitness value x.

SUS and this Biased SUS are similar to fitness proportional selection and a biased variation of fitness proportional selection. However, whereas fitness proportional selection is like spinning a carnival wheel with a single pointer M times to select M members of the population, SUS instead is like spinning a carnival wheel that has M equidistant pointers a single time to select all M simultaneously. One statistical consequence of this is that it reduces the variance of the selected copies of population members as compared to fitness proportional selection (and its biased variation). Another consequence is that SUS and Biased SUS are typically much faster since only a single random floating point number is needed per generation, compared to M random floating-point numbers for fitness proportional selection. However, SUS and Biased SUS then must randomize the ordering of the population to avoid all of the copies of a single population member from being in sequence so that parent assignment is random, whereas fitness proportional selection has this property built in.

This selection operator requires positive fitness values. Behavior is undefined if any fitness values are less than or equal to 0. If your fitness values may be negative, you can use FitnessShifter, which transforms fitness values such that minimum fitness equals 1.

The runtime to select M population members from a population of size N is O(N + M), which includes the need to generate only a single random double, and O(M) ints. This assumes that the bias function has a constant runtime.

For the more common standard version of SUS, see the StochasticUniversalSampling class.

  • Constructor Details

    • BiasedStochasticUniversalSampling

      public BiasedStochasticUniversalSampling(FitnessBiasFunction bias)
      Construct a biased stochastic universal sampling operator.
      Parameters:
      bias - A bias function

  • Method Details

    • split

      public BiasedStochasticUniversalSampling 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>
      Overrides:
      split in class StochasticUniversalSampling
      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.

    • select

      public final 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 final 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().