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

Class BiasedShiftedStochasticUniversalSampling

java.lang.Object
org.cicirello.search.evo.StochasticUniversalSampling
org.cicirello.search.evo.BiasedShiftedStochasticUniversalSampling
All Implemented Interfaces:
Splittable<SelectionOperator>, SelectionOperator
@Deprecated public final class BiasedShiftedStochasticUniversalSampling extends StochasticUniversalSampling
Deprecated.
Instead of this class, you should use a combination of FitnessShifter and BiasedStochasticUniversalSampling. This class is scheduled for removal in release 6.0.0.

This class implements a variation of Stochastic Universal Sampling (SUS) that we call Biased Shifted Stochastic Universal Sampling (Biased Shifted SUS), which uses shifted fitness values and integrates the use of a bias function with SUS to enable transforming the shifted fitness values prior to the stochastic selection decisions. Specifically, first it shifts all fitness values by the minimum fitness minus one, such that the least fit population member's selection probability is based on a transformed fitness equal to 1. Next, a member of the population is chosen randomly with probability proportional to a bias function of this shifted fitness relative to the total of such biased shifted fitness of the population. For example, if the fitness of population member i is fi, and if the minimum fitness in the population is fmin, then the probability of selecting population member i is: (bias(fi) - fmin + 1) / ∑j (bias(fj) - fmin + 1), 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 Shifted SUS are similar to 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 and this variation of 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. Another consequence is that SUS is 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 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 is compatible with all fitness functions, even in the case of negative fitness values.

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.

  • Constructor Details

    • BiasedShiftedStochasticUniversalSampling

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

  • Method Details

    • split

      public BiasedShiftedStochasticUniversalSampling split()
      Deprecated.
      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().