Uses of Interface
org.cicirello.search.evo.FitnessFunction.Integer

Packages that use FitnessFunction.Integer

Package	Description
org.cicirello.search.evo	This package includes classes and interfaces directly related to implementing evolutionary algorithms.

Uses of FitnessFunction.Integer in org.cicirello.search.evo

Classes in org.cicirello.search.evo that implement FitnessFunction.Integer

Modifier and Type	Class	Description
final class	NegativeIntegerCostFitnessFunction<T extends Copyable<T>>	This class provides a convenient mechanism for transforming optimization cost values to fitness values.

Constructors in org.cicirello.search.evo with parameters of type FitnessFunction.Integer

Modifier	Constructor	Description
	AdaptiveEvolutionaryAlgorithm(int n, MutationOperator<T> mutation, CrossoverOperator<T> crossover, Initializer<T> initializer, FitnessFunction.Integer<T> f, SelectionOperator selection)	Constructs and initializes the evolutionary algorithm.
	AdaptiveEvolutionaryAlgorithm(int n, MutationOperator<T> mutation, CrossoverOperator<T> crossover, Initializer<T> initializer, FitnessFunction.Integer<T> f, SelectionOperator selection, int eliteCount)	Constructs and initializes the evolutionary algorithm.
	AdaptiveEvolutionaryAlgorithm(int n, MutationOperator<T> mutation, CrossoverOperator<T> crossover, Initializer<T> initializer, FitnessFunction.Integer<T> f, SelectionOperator selection, int eliteCount, ProgressTracker<T> tracker)	Constructs and initializes the evolutionary algorithm.
	AdaptiveEvolutionaryAlgorithm(int n, MutationOperator<T> mutation, CrossoverOperator<T> crossover, Initializer<T> initializer, FitnessFunction.Integer<T> f, SelectionOperator selection, ProgressTracker<T> tracker)	Constructs and initializes the evolutionary algorithm.
	AdaptiveMutationOnlyEvolutionaryAlgorithm(int n, MutationOperator<T> mutation, Initializer<T> initializer, FitnessFunction.Integer<T> f, SelectionOperator selection)	Constructs and initializes the evolutionary algorithm.
	AdaptiveMutationOnlyEvolutionaryAlgorithm(int n, MutationOperator<T> mutation, Initializer<T> initializer, FitnessFunction.Integer<T> f, SelectionOperator selection, int eliteCount)	Constructs and initializes the evolutionary algorithm.
	AdaptiveMutationOnlyEvolutionaryAlgorithm(int n, MutationOperator<T> mutation, Initializer<T> initializer, FitnessFunction.Integer<T> f, SelectionOperator selection, int eliteCount, ProgressTracker<T> tracker)	Constructs and initializes the evolutionary algorithm.
	AdaptiveMutationOnlyEvolutionaryAlgorithm(int n, MutationOperator<T> mutation, Initializer<T> initializer, FitnessFunction.Integer<T> f, SelectionOperator selection, ProgressTracker<T> tracker)	Constructs and initializes the evolutionary algorithm.
	GenerationalEvolutionaryAlgorithm(int n, MutationOperator<T> mutation, double mutationRate, CrossoverOperator<T> crossover, double crossoverRate, Initializer<T> initializer, FitnessFunction.Integer<T> f, SelectionOperator selection)	Constructs and initializes the evolutionary algorithm.
	GenerationalEvolutionaryAlgorithm(int n, MutationOperator<T> mutation, double mutationRate, CrossoverOperator<T> crossover, double crossoverRate, Initializer<T> initializer, FitnessFunction.Integer<T> f, SelectionOperator selection, int eliteCount)	Constructs and initializes the evolutionary algorithm.
	GenerationalEvolutionaryAlgorithm(int n, MutationOperator<T> mutation, double mutationRate, CrossoverOperator<T> crossover, double crossoverRate, Initializer<T> initializer, FitnessFunction.Integer<T> f, SelectionOperator selection, int eliteCount, ProgressTracker<T> tracker)	Constructs and initializes the evolutionary algorithm.
	GenerationalEvolutionaryAlgorithm(int n, MutationOperator<T> mutation, double mutationRate, CrossoverOperator<T> crossover, double crossoverRate, Initializer<T> initializer, FitnessFunction.Integer<T> f, SelectionOperator selection, ProgressTracker<T> tracker)	Constructs and initializes the evolutionary algorithm.
	GenerationalEvolutionaryAlgorithmMutuallyExclusiveOperators(int n, MutationOperator<T> mutation, double mutationRate, CrossoverOperator<T> crossover, double crossoverRate, Initializer<T> initializer, FitnessFunction.Integer<T> f, SelectionOperator selection)	Constructs and initializes the evolutionary algorithm for an EA utilizing both a crossover operator and a mutation operator, such that the genetic operators follow a mutually exclusive property where each population member is involved in at most one of those operations in a single generation.
	GenerationalEvolutionaryAlgorithmMutuallyExclusiveOperators(int n, MutationOperator<T> mutation, double mutationRate, CrossoverOperator<T> crossover, double crossoverRate, Initializer<T> initializer, FitnessFunction.Integer<T> f, SelectionOperator selection, int eliteCount)	Constructs and initializes the evolutionary algorithm for an EA utilizing both a crossover operator and a mutation operator, such that the genetic operators follow a mutually exclusive property where each population member is involved in at most one of those operations in a single generation.
	GenerationalEvolutionaryAlgorithmMutuallyExclusiveOperators(int n, MutationOperator<T> mutation, double mutationRate, CrossoverOperator<T> crossover, double crossoverRate, Initializer<T> initializer, FitnessFunction.Integer<T> f, SelectionOperator selection, int eliteCount, ProgressTracker<T> tracker)	Constructs and initializes the evolutionary algorithm for an EA utilizing both a crossover operator and a mutation operator, such that the genetic operators follow a mutually exclusive property where each population member is involved in at most one of those operations in a single generation.
	GenerationalEvolutionaryAlgorithmMutuallyExclusiveOperators(int n, MutationOperator<T> mutation, double mutationRate, CrossoverOperator<T> crossover, double crossoverRate, Initializer<T> initializer, FitnessFunction.Integer<T> f, SelectionOperator selection, ProgressTracker<T> tracker)	Constructs and initializes the evolutionary algorithm for an EA utilizing both a crossover operator and a mutation operator, such that the genetic operators follow a mutually exclusive property where each population member is involved in at most one of those operations in a single generation.
	GenerationalMutationOnlyEvolutionaryAlgorithm(int n, MutationOperator<T> mutation, double mutationRate, Initializer<T> initializer, FitnessFunction.Integer<T> f, SelectionOperator selection)	Constructs and initializes the evolutionary algorithm with mutation only.
	GenerationalMutationOnlyEvolutionaryAlgorithm(int n, MutationOperator<T> mutation, double mutationRate, Initializer<T> initializer, FitnessFunction.Integer<T> f, SelectionOperator selection, int eliteCount)	Constructs and initializes the evolutionary algorithm with mutation only.
	GenerationalMutationOnlyEvolutionaryAlgorithm(int n, MutationOperator<T> mutation, double mutationRate, Initializer<T> initializer, FitnessFunction.Integer<T> f, SelectionOperator selection, int eliteCount, ProgressTracker<T> tracker)	Constructs and initializes the evolutionary algorithm with mutation only.
	GenerationalMutationOnlyEvolutionaryAlgorithm(int n, MutationOperator<T> mutation, double mutationRate, Initializer<T> initializer, FitnessFunction.Integer<T> f, SelectionOperator selection, ProgressTracker<T> tracker)	Constructs and initializes the evolutionary algorithm with mutation only.
	GeneticAlgorithm(int n, int bitLength, FitnessFunction.Integer<BitVector> f, double mutationRate, CrossoverOperator<BitVector> crossover, double crossoverRate, SelectionOperator selection)	Initializes a genetic algorithm with a generational model where children replace the parents, using the standard bit flip mutation.
	GeneticAlgorithm(int n, int bitLength, FitnessFunction.Integer<BitVector> f, double mutationRate, CrossoverOperator<BitVector> crossover, double crossoverRate, SelectionOperator selection, int eliteCount)	Initializes a genetic algorithm with a generational model where children replace the parents, using the standard bit flip mutation.
	GeneticAlgorithm(int n, int bitLength, FitnessFunction.Integer<BitVector> f, double mutationRate, CrossoverOperator<BitVector> crossover, double crossoverRate, SelectionOperator selection, int eliteCount, ProgressTracker<BitVector> tracker)	Initializes a genetic algorithm with a generational model where children replace the parents, using the standard bit flip mutation.
	GeneticAlgorithm(int n, int bitLength, FitnessFunction.Integer<BitVector> f, double mutationRate, CrossoverOperator<BitVector> crossover, double crossoverRate, SelectionOperator selection, ProgressTracker<BitVector> tracker)	Initializes a genetic algorithm with a generational model where children replace the parents, using the standard bit flip mutation.
	GeneticAlgorithm(int n, Initializer<BitVector> initializer, FitnessFunction.Integer<BitVector> f, double mutationRate, CrossoverOperator<BitVector> crossover, double crossoverRate, SelectionOperator selection)	Initializes a genetic algorithm with a generational model where children replace the parents, using the standard bit flip mutation.
	GeneticAlgorithm(int n, Initializer<BitVector> initializer, FitnessFunction.Integer<BitVector> f, double mutationRate, CrossoverOperator<BitVector> crossover, double crossoverRate, SelectionOperator selection, int eliteCount)	Initializes a genetic algorithm with a generational model where children replace the parents, using the standard bit flip mutation.
	GeneticAlgorithm(int n, Initializer<BitVector> initializer, FitnessFunction.Integer<BitVector> f, double mutationRate, CrossoverOperator<BitVector> crossover, double crossoverRate, SelectionOperator selection, int eliteCount, ProgressTracker<BitVector> tracker)	Initializes a genetic algorithm with a generational model where children replace the parents, using the standard bit flip mutation.
	GeneticAlgorithm(int n, Initializer<BitVector> initializer, FitnessFunction.Integer<BitVector> f, double mutationRate, CrossoverOperator<BitVector> crossover, double crossoverRate, SelectionOperator selection, ProgressTracker<BitVector> tracker)	Initializes a genetic algorithm with a generational model where children replace the parents, using the standard bit flip mutation.
	MutationOnlyGeneticAlgorithm(int n, int bitLength, FitnessFunction.Integer<BitVector> f, double mutationRate, SelectionOperator selection)	Initializes a mutation-only genetic algorithm with a generational model where children replace the parents, using the standard bit flip mutation.
	MutationOnlyGeneticAlgorithm(int n, int bitLength, FitnessFunction.Integer<BitVector> f, double mutationRate, SelectionOperator selection, int eliteCount)	Initializes a mutation-only genetic algorithm with a generational model where children replace the parents, using the standard bit flip mutation.
	MutationOnlyGeneticAlgorithm(int n, int bitLength, FitnessFunction.Integer<BitVector> f, double mutationRate, SelectionOperator selection, int eliteCount, ProgressTracker<BitVector> tracker)	Initializes a mutation-only genetic algorithm with a generational model where children replace the parents, using the standard bit flip mutation.
	MutationOnlyGeneticAlgorithm(int n, int bitLength, FitnessFunction.Integer<BitVector> f, double mutationRate, SelectionOperator selection, ProgressTracker<BitVector> tracker)	Initializes a mutation-only genetic algorithm with a generational model where children replace the parents, using the standard bit flip mutation.
	MutationOnlyGeneticAlgorithm(int n, Initializer<BitVector> initializer, FitnessFunction.Integer<BitVector> f, double mutationRate, SelectionOperator selection)	Initializes a mutation-only genetic algorithm with a generational model where children replace the parents, using the standard bit flip mutation.
	MutationOnlyGeneticAlgorithm(int n, Initializer<BitVector> initializer, FitnessFunction.Integer<BitVector> f, double mutationRate, SelectionOperator selection, int eliteCount)	Initializes a mutation-only genetic algorithm with a generational model where children replace the parents, using the standard bit flip mutation.
	MutationOnlyGeneticAlgorithm(int n, Initializer<BitVector> initializer, FitnessFunction.Integer<BitVector> f, double mutationRate, SelectionOperator selection, int eliteCount, ProgressTracker<BitVector> tracker)	Initializes a mutation-only genetic algorithm with a generational model where children replace the parents, using the standard bit flip mutation.
	MutationOnlyGeneticAlgorithm(int n, Initializer<BitVector> initializer, FitnessFunction.Integer<BitVector> f, double mutationRate, SelectionOperator selection, ProgressTracker<BitVector> tracker)	Initializes a mutation-only genetic algorithm with a generational model where children replace the parents, using the standard bit flip mutation.
	NaiveGenerationalEvolutionaryAlgorithm(int n, MutationOperator<T> mutation, double mutationRate, CrossoverOperator<T> crossover, double crossoverRate, Initializer<T> initializer, FitnessFunction.Integer<T> f, SelectionOperator selection)	Deprecated. Constructs and initializes the evolutionary algorithm.
	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).
	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).