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

Class SimpleGeneticAlgorithm

java.lang.Object
org.cicirello.search.evo.GenerationalEvolutionaryAlgorithm<BitVector>
org.cicirello.search.evo.GeneticAlgorithm
org.cicirello.search.evo.SimpleGeneticAlgorithm
All Implemented Interfaces:
Splittable<TrackableSearch<BitVector>>, Metaheuristic<BitVector>, ReoptimizableMetaheuristic<BitVector>, TrackableSearch<BitVector>
public final class SimpleGeneticAlgorithm extends GeneticAlgorithm
This class is an implementation of the simple genetic algorithm (Simple GA) with the common bit vector representation of solutions to optimization problems, and the generational model where children replace their parents each generation. It uses the usual bit flip mutation, where each bit of each member of the population is mutated (flipped) with some probability, known as the mutation rate, each generation. The crossover operator is single-point crossover (see the SinglePointCrossover class), and the selection operator is fitness proportional (see the FitnessProportionalSelection class).

The library also includes other classes for evolutionary algorithms that may be more relevant depending upon your use-case. For example, see the GeneticAlgorithm class for greater flexibility in configuring the crossover and selection operators, the MutationOnlyGeneticAlgorithm class if all you want to use is mutation and no crossover, and the GenerationalEvolutionaryAlgorithm class if you want to optimize something other than BitVectors or if you want even greater flexibility in configuring your evolutionary search.

  • Constructor Details

    • SimpleGeneticAlgorithm

      public SimpleGeneticAlgorithm(int n, int bitLength, FitnessFunction.Double<BitVector> f, double mutationRate, double crossoverRate, ProgressTracker<BitVector> tracker)
      Initializes a simple genetic algorithm with a generational model where children replace the parents, using the standard bit flip mutation, single-point crossover (the SinglePointCrossover class), and fitness-proportional selection (the FitnessProportionalSelection class). This constructor supports fitness functions with fitnesses of type double, the FitnessFunction.Double interface.
      Parameters:
      n - The population size.
      bitLength - The length of each bit vector.
      f - The fitness function.
      mutationRate - The per-bit probability of flipping a bit. Each bit of each member of the population is flipped with this probability, and the decisions to flip bits are independent.
      crossoverRate - The probability that a pair of parents undergo crossover.
      tracker - A ProgressTracker.
      Throws:
      IllegalArgumentException - if n is less than 1.
      IllegalArgumentException - if mutationRate ≤ 0 or if mutationRate ≥ 1.
      IllegalArgumentException - if crossoverRate is less than 0.
      IllegalArgumentException - if bitLength is negative.
      NullPointerException - if any of f, or tracker are null.

    • SimpleGeneticAlgorithm

      public SimpleGeneticAlgorithm(int n, int bitLength, FitnessFunction.Integer<BitVector> f, double mutationRate, double crossoverRate, ProgressTracker<BitVector> tracker)
      Initializes a simple genetic algorithm with a generational model where children replace the parents, using the standard bit flip mutation, single-point crossover (the SinglePointCrossover class), and fitness-proportional selection (the FitnessProportionalSelection class). This constructor supports fitness functions with fitnesses of type int, the FitnessFunction.Integer interface.
      Parameters:
      n - The population size.
      bitLength - The length of each bit vector.
      f - The fitness function.
      mutationRate - The per-bit probability of flipping a bit. Each bit of each member of the population is flipped with this probability, and the decisions to flip bits are independent.
      crossoverRate - The probability that a pair of parents undergo crossover.
      tracker - A ProgressTracker.
      Throws:
      IllegalArgumentException - if n is less than 1.
      IllegalArgumentException - if mutationRate ≤ 0 or if mutationRate ≥ 1.
      IllegalArgumentException - if crossoverRate is less than 0.
      IllegalArgumentException - if bitLength is negative.
      NullPointerException - if any of f, or tracker are null.

    • SimpleGeneticAlgorithm

      public SimpleGeneticAlgorithm(int n, int bitLength, FitnessFunction.Double<BitVector> f, double mutationRate, double crossoverRate)
      Initializes a simple genetic algorithm with a generational model where children replace the parents, using the standard bit flip mutation, single-point crossover (the SinglePointCrossover class), and fitness-proportional selection (the FitnessProportionalSelection class). This constructor supports fitness functions with fitnesses of type double, the FitnessFunction.Double interface.
      Parameters:
      n - The population size.
      bitLength - The length of each bit vector.
      f - The fitness function.
      mutationRate - The per-bit probability of flipping a bit. Each bit of each member of the population is flipped with this probability, and the decisions to flip bits are independent.
      crossoverRate - The probability that a pair of parents undergo crossover.
      Throws:
      IllegalArgumentException - if n is less than 1.
      IllegalArgumentException - if mutationRate ≤ 0 or if mutationRate ≥ 1.
      IllegalArgumentException - if crossoverRate is less than 0.
      IllegalArgumentException - if bitLength is negative.
      NullPointerException - if f is null.

    • SimpleGeneticAlgorithm

      public SimpleGeneticAlgorithm(int n, int bitLength, FitnessFunction.Integer<BitVector> f, double mutationRate, double crossoverRate)
      Initializes a simple genetic algorithm with a generational model where children replace the parents, using the standard bit flip mutation, single-point crossover (the SinglePointCrossover class), and fitness-proportional selection (the FitnessProportionalSelection class). This constructor supports fitness functions with fitnesses of type int, the FitnessFunction.Integer interface.
      Parameters:
      n - The population size.
      bitLength - The length of each bit vector.
      f - The fitness function.
      mutationRate - The per-bit probability of flipping a bit. Each bit of each member of the population is flipped with this probability, and the decisions to flip bits are independent.
      crossoverRate - The probability that a pair of parents undergo crossover.
      Throws:
      IllegalArgumentException - if n is less than 1.
      IllegalArgumentException - if mutationRate ≤ 0 or if mutationRate ≥ 1.
      IllegalArgumentException - if crossoverRate is less than 0.
      IllegalArgumentException - if bitLength is negative.
      NullPointerException - if f is null.

  • Method Details

    • split

      public SimpleGeneticAlgorithm 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<BitVector>
      Specified by:
      split in interface ReoptimizableMetaheuristic<BitVector>
      Specified by:
      split in interface Splittable<TrackableSearch<BitVector>>
      Overrides:
      split in class GeneticAlgorithm
      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.

    • optimize

      public final SolutionCostPair<BitVector> optimize(int numGenerations)
      Runs the evolutionary algorithm beginning from a randomly generated population. If this method is called multiple times, each call begins at a new randomly generated population.
      Specified by:
      optimize in interface Metaheuristic<T extends Copyable<T>>
      Parameters:
      numGenerations - The number of generations to run.
      Returns:
      The best solution found during this set of generations, which may or may not be the same as the solution contained in the ProgressTracker, which contains the best across all calls to optimize as well as ReoptimizableMetaheuristic.reoptimize(int). Returns null if the run did not execute, such as if the ProgressTracker already contains the theoretical best solution.

    • reoptimize

      public final SolutionCostPair<BitVector> reoptimize(int numGenerations)
      Runs the evolutionary algorithm continuing from the final population from the most recent call to either Metaheuristic.optimize(int) or ReoptimizableMetaheuristic.reoptimize(int), or from a random population if this is the first call to either method.
      Specified by:
      reoptimize in interface ReoptimizableMetaheuristic<T extends Copyable<T>>
      Parameters:
      numGenerations - The number of generations to run.
      Returns:
      The best solution found during this set of generations, which may or may not be the same as the solution contained in the ProgressTracker, which contains the best across all calls to optimize as well as Metaheuristic.optimize(int). Returns null if the run did not execute, such as if the ProgressTracker already contains the theoretical best solution.

    • getProgressTracker

      public final ProgressTracker<BitVector> getProgressTracker()
      Description copied from interface: TrackableSearch
      Gets the ProgressTracker object that is in use for tracking search progress. The object returned by this method contains the best solution found during the search (including across multiple concurrent runs if the search is multithreaded, or across multiple restarts if the run methods were called multiple times), as well as cost of that solution, among other information. See the ProgressTracker documentation for more information about the search data tracked by this object.
      Specified by:
      getProgressTracker in interface TrackableSearch<T extends Copyable<T>>
      Returns:
      the ProgressTracker in use by this metaheuristic.

    • setProgressTracker

      public final void setProgressTracker(ProgressTracker<BitVector> tracker)
      Description copied from interface: TrackableSearch
      Sets the ProgressTracker object that is in use for tracking search progress. Any previously set ProgressTracker is replaced by this one.
      Specified by:
      setProgressTracker in interface TrackableSearch<T extends Copyable<T>>
      Parameters:
      tracker - The new ProgressTracker to set. The tracker must not be null. This method does nothing if tracker is null.

    • getProblem

      public final Problem<BitVector> getProblem()
      Description copied from interface: TrackableSearch
      Gets a reference to the problem that this search is solving.
      Specified by:
      getProblem in interface TrackableSearch<T extends Copyable<T>>
      Returns:
      a reference to the problem.

    • getTotalRunLength

      public long getTotalRunLength()
      Gets the total run length in number of fitness evaluations. This is the total run length across all calls to Metaheuristic.optimize(int) and ReoptimizableMetaheuristic.reoptimize(int). This may differ from what may be expected based on run lengths. For example, the search terminates if it finds the theoretical best solution, and also immediately returns if a prior call found the theoretical best. In such cases, the total run length may be less than the requested run length.
      Specified by:
      getTotalRunLength in interface TrackableSearch<T extends Copyable<T>>
      Returns:
      The total number of generations completed across all calls to Metaheuristic.optimize(int) and ReoptimizableMetaheuristic.reoptimize(int).