Class TournamentSelection

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

public final class TournamentSelection extends Object implements SelectionOperator

This class implements tournament selection for evolutionary algorithms. In tournament selection, a member of the population is chosen in the following manner. First, choose k members of the population uniformly at random (with replacement). Next, from those k members, select the one with greatest fitness. Repeat this process as many times as needed to form the population for the generation. When k=2, it is known as binary tournament selection.

This selection operator is compatible with all fitness functions, even in the case of negative fitness values, since it simply compares which fitness values are higher.

The runtime to select M population members from a population of size N is O(k M), which includes generating O(k M) random int values.

  • Constructor Details

    • TournamentSelection

      public TournamentSelection()
      Constructs a binary tournament selection operator, i.e., k = 2.
    • TournamentSelection

      public TournamentSelection(int k)
      Constructs a tournament selection operator.
      Parameters:
      k - The tournament size, which must be at least 2.
      Throws:
      IllegalArgumentException - if k is less than 2.
  • 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().
    • split

      public TournamentSelection 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.