Interface PopulationMetaheuristic<T extends Copyable<T>>

Type Parameters:
T - The type of object under optimization.
All Superinterfaces:
Metaheuristic<T>, ReoptimizableMetaheuristic<T>, Splittable<TrackableSearch<T>>, TrackableSearch<T>
All Known Implementing Classes:
AdaptiveEvolutionaryAlgorithm, AdaptiveMutationOnlyEvolutionaryAlgorithm, GenerationalDisjointOperatorsEvolutionaryAlgorithm, GenerationalEvolutionaryAlgorithm, GenerationalMutationOnlyEvolutionaryAlgorithm, GeneticAlgorithm, MutationOnlyGeneticAlgorithm, SimpleGeneticAlgorithm
public interface PopulationMetaheuristic<T extends Copyable<T>> extends ReoptimizableMetaheuristic<T>
This interface defines the required methods for implementations of metaheuristics that maintain a population of candidate solutions such as evolutionary algorithms. The primary additional functionality defined over that already included in the parent interface is the ability to define termination criteria based on the state of the underlying population of solutions.

  • Method Details

    • optimize

      SolutionCostPair<T> optimize(int numGenerations, TerminationStrategy<T> terminator)
      Executes a run of the metaheuristic beginning at a randomly generated population of solutions. If this method is called multiple times, each call begins at a new randomly generated starting population, and reinitializes any control parameters of the metaheuristic that may have changed during the previous call to optimize to start of run state.
      Parameters:
      numGenerations - The maximum number of generations, which will cause the run to terminate even if other criteria specified in the terminator are not currently met. Some selection operators and replacement strategies may need to know the maximum number of generations to operate properly, which is why this parameter is needed separately from other termination criteria.
      terminator - The termination strategy, such that the optimize method runs until that strategy is satisfied.
      Returns:
      The current solution at the end of this run and its cost, which may or may not be the best of run solution, and which may or may not be the same as the solution contained in this metaheuristic's ProgressTracker, which contains the best of all runs. Returns null if the run did not execute, such as if the ProgressTracker already contains the theoretical best solution.

    • split

      PopulationMetaheuristic<T> 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 Metaheuristic<T extends Copyable<T>>
      Specified by:
      split in interface ReoptimizableMetaheuristic<T extends Copyable<T>>
      Specified by:
      split in interface Splittable<T extends Copyable<T>>
      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.