 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 singlepoint crossover (see the
SinglePointCrossover
class), and the selection operator is fitness proportional (see theFitnessProportionalSelection
class).The library also includes other classes for evolutionary algorithms that may be more relevant depending upon your usecase. For example, see the
GeneticAlgorithm
class for greater flexibility in configuring the crossover and selection operators, theMutationOnlyGeneticAlgorithm
class if all you want to use is mutation and no crossover, and theGenerationalEvolutionaryAlgorithm
class if you want to optimize something other than BitVectors or if you want even greater flexibility in configuring your evolutionary search.


Constructor Summary
Constructors Constructor Description 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, singlepoint crossover (theSinglePointCrossover
class), and fitnessproportional selection (theFitnessProportionalSelection
class).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, singlepoint crossover (theSinglePointCrossover
class), and fitnessproportional selection (theFitnessProportionalSelection
class).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, singlepoint crossover (theSinglePointCrossover
class), and fitnessproportional selection (theFitnessProportionalSelection
class).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, singlepoint crossover (theSinglePointCrossover
class), and fitnessproportional selection (theFitnessProportionalSelection
class).

Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description Problem<T>
getProblem()
Gets a reference to the problem that this search is solving.ProgressTracker<T>
getProgressTracker()
Gets theProgressTracker
object that is in use for tracking search progress.long
getTotalRunLength()
Gets the total run length in number of fitness evaluations.SolutionCostPair<T>
optimize(int numGenerations)
Runs the evolutionary algorithm beginning from a randomly generated population.SolutionCostPair<T>
reoptimize(int numGenerations)
Runs the evolutionary algorithm continuing from the final population from the most recent call to eitherMetaheuristic.optimize(int)
orReoptimizableMetaheuristic.reoptimize(int)
, or from a random population if this is the first call to either method.void
setProgressTracker(ProgressTracker<T> tracker)
Sets theProgressTracker
object that is in use for tracking search progress.SimpleGeneticAlgorithm
split()
Generates a functionally identical copy of this object, for use in multithreaded implementations of search algorithms.



Constructor Detail

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, singlepoint crossover (the
SinglePointCrossover
class), and fitnessproportional selection (theFitnessProportionalSelection
class). This constructor supports fitness functions with fitnesses of type double, theFitnessFunction.Double
interface. Parameters:
n
 The population size.bitLength
 The length of each bit vector.f
 The fitness function.mutationRate
 The perbit 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, singlepoint crossover (the
SinglePointCrossover
class), and fitnessproportional selection (theFitnessProportionalSelection
class). This constructor supports fitness functions with fitnesses of type int, theFitnessFunction.Integer
interface. Parameters:
n
 The population size.bitLength
 The length of each bit vector.f
 The fitness function.mutationRate
 The perbit 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, singlepoint crossover (the
SinglePointCrossover
class), and fitnessproportional selection (theFitnessProportionalSelection
class). This constructor supports fitness functions with fitnesses of type double, theFitnessFunction.Double
interface. Parameters:
n
 The population size.bitLength
 The length of each bit vector.f
 The fitness function.mutationRate
 The perbit 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, singlepoint crossover (the
SinglePointCrossover
class), and fitnessproportional selection (theFitnessProportionalSelection
class). This constructor supports fitness functions with fitnesses of type int, theFitnessFunction.Integer
interface. Parameters:
n
 The population size.bitLength
 The length of each bit vector.f
 The fitness function.mutationRate
 The perbit 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 Detail

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 theCopyable
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 interfaceMetaheuristic<BitVector>
 Specified by:
split
in interfaceReoptimizableMetaheuristic<BitVector>
 Specified by:
split
in interfaceSplittable<TrackableSearch<BitVector>>
 Overrides:
split
in classGeneticAlgorithm
 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<T> 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 interfaceMetaheuristic<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 asReoptimizableMetaheuristic.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<T> reoptimize(int numGenerations)
Runs the evolutionary algorithm continuing from the final population from the most recent call to eitherMetaheuristic.optimize(int)
orReoptimizableMetaheuristic.reoptimize(int)
, or from a random population if this is the first call to either method. Specified by:
reoptimize
in interfaceReoptimizableMetaheuristic<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 asMetaheuristic.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<T> getProgressTracker()
Description copied from interface:TrackableSearch
Gets theProgressTracker
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 theProgressTracker
documentation for more information about the search data tracked by this object. Specified by:
getProgressTracker
in interfaceTrackableSearch<T extends Copyable<T>>
 Returns:
 the
ProgressTracker
in use by this metaheuristic.

setProgressTracker
public final void setProgressTracker(ProgressTracker<T> tracker)
Description copied from interface:TrackableSearch
Sets theProgressTracker
object that is in use for tracking search progress. Any previously set ProgressTracker is replaced by this one. Specified by:
setProgressTracker
in interfaceTrackableSearch<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<T> getProblem()
Description copied from interface:TrackableSearch
Gets a reference to the problem that this search is solving. Specified by:
getProblem
in interfaceTrackableSearch<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 toMetaheuristic.optimize(int)
andReoptimizableMetaheuristic.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 interfaceTrackableSearch<T extends Copyable<T>>
 Returns:
 The total number of generations completed across all calls to
Metaheuristic.optimize(int)
andReoptimizableMetaheuristic.reoptimize(int)
.

