Uses of Interface
org.cicirello.search.TrackableSearch

Packages that use TrackableSearch

Package	Description
org.cicirello.search	This package includes classes and interfaces related to implementing metaheuristic search algorithms in general, rather than specific to a particular metaheuristic.
org.cicirello.search.concurrent	This package includes multithreaded search implementations, as well as classes and interfaces related to implementing multithreaded metaheuristics.
org.cicirello.search.evo	This package includes classes and interfaces directly related to implementing evolutionary algorithms.
org.cicirello.search.hc	This package includes classes and interfaces directly related to implementing hill climbers.
org.cicirello.search.restarts	This package includes classes and interfaces related to implementing multistart metaheuristics (i.e., metaheuristics that periodically restart, and return the best solution across a number of such restarts).
org.cicirello.search.sa	This package includes classes and interfaces directly related to implementing simulated annealing.
org.cicirello.search.ss	This package includes classes and interfaces directly related to implementing stochastic sampling algorithms.

Uses of TrackableSearch in org.cicirello.search

Subinterfaces of TrackableSearch in org.cicirello.search

Modifier and Type	Interface	Description
interface	Metaheuristic<T extends Copyable<T>>	This interface defines the required methods for implementations of metaheuristics, in particular metaheuristics for which the maximum run length can be specified.
interface	ReoptimizableMetaheuristic<T extends Copyable<T>>	This interface defines the required methods for implementations of metaheuristics, for which the maximum run length can be specified, and which have the capability of restarting from a previously optimized solution.
interface	SimpleLocalMetaheuristic<T extends Copyable<T>>	This interface defines the required methods for implementations of simple metaheuristics that locally optimize from some initial solution (random or otherwise) whose run length is self-determined, such as hill climbers that terminate upon reaching a local optima.
interface	SimpleMetaheuristic<T extends Copyable<T>>	This interface defines the required methods for implementations of simple metaheuristics whose run length is self-determined, such as hill climbers that terminate upon reaching a local optima.
interface	SingleSolutionMetaheuristic<T extends Copyable<T>>	This interface defines the required methods for implementations of single-solution metaheuristics, i.e., metaheuristics such as simulated annealing that operate one a single candidate solution (as compared to population-based metaheuristics such as genetic algorithms.

Uses of TrackableSearch in org.cicirello.search.concurrent

Classes in org.cicirello.search.concurrent that implement TrackableSearch

Modifier and Type	Class	Description
class	ParallelMetaheuristic<T extends Copyable<T>>	This class enables running multiple copies of a metaheuristic, or multiple metaheuristics, in parallel with multiple threads.
final class	ParallelMultistarter<T extends Copyable<T>>	This class is used for implementing parallel multistart metaheuristics.
class	ParallelReoptimizableMetaheuristic<T extends Copyable<T>>	This class enables running multiple copies of a metaheuristic, or multiple metaheuristics, in parallel with multiple threads.
final class	ParallelReoptimizableMultistarter<T extends Copyable<T>>	This class is used for implementing parallel multistart metaheuristics.
class	TimedParallelMultistarter<T extends Copyable<T>>	This class is used for implementing parallel multistart metaheuristics.
final class	TimedParallelReoptimizableMultistarter<T extends Copyable<T>>	This class is used for implementing parallel multistart metaheuristics.

Uses of TrackableSearch in org.cicirello.search.evo

Classes in org.cicirello.search.evo that implement TrackableSearch

Modifier and Type	Class	Description
class	AdaptiveEvolutionaryAlgorithm<T extends Copyable<T>>	This class implements an evolutionary algorithm with adaptive control parameters (i.e., crossover rates and mutation rates that evolve during the search).
class	AdaptiveMutationOnlyEvolutionaryAlgorithm<T extends Copyable<T>>	This class implements an mutation-only evolutionary algorithm with an adaptive mutation rate that evolves during the search.
class	GenerationalEvolutionaryAlgorithm<T extends Copyable<T>>	This class implements an evolutionary algorithm with a generational model, such as is commonly used in genetic algorithms, where a population of children are formed by applying genetic operators to members of the parent population, and where the children replace the parents in the next generation.
class	GenerationalEvolutionaryAlgorithmMutuallyExclusiveOperators<T extends Copyable<T>>	This class implements an evolutionary algorithm (EA) with a generational model, where a population of children are formed by applying genetic operators to members of the parent population, and where the children replace the parents in the next generation.
class	GenerationalMutationOnlyEvolutionaryAlgorithm<T extends Copyable<T>>	This class implements an evolutionary algorithm (EA) with a generational model, such as is commonly used in genetic algorithms, where a population of children are formed by applying mutation to members of the parent population, and where the children replace the parents in the next generation.
class	GeneticAlgorithm	This class is an implementation of a genetic algorithm (GA) with the common bit vector representation of solutions to optimization problems, and the generational model where children replace their parents each generation.
final class	MutationOnlyGeneticAlgorithm	This class is an implementation of a mutation-only genetic algorithm (GA) with the common bit vector representation of solutions to optimization problems, and the generational model where children replace their parents each generation.
class	OnePlusOneEvolutionaryAlgorithm<T extends Copyable<T>>	This class implements a (1+1)-EA.
final class	OnePlusOneGeneticAlgorithm	This class implements a (1+1)-GA, a special case of a (1+1)-EA, where solutions are represented with a vector of bits.
final class	SimpleGeneticAlgorithm	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.

Uses of TrackableSearch in org.cicirello.search.hc

Classes in org.cicirello.search.hc that implement TrackableSearch

Modifier and Type	Class	Description
final class	FirstDescentHillClimber<T extends Copyable<T>>	This class implements first descent hill climbing.
final class	SteepestDescentHillClimber<T extends Copyable<T>>	This class implements steepest descent hill climbing.

Uses of TrackableSearch in org.cicirello.search.restarts

Classes in org.cicirello.search.restarts that implement TrackableSearch

Modifier and Type	Class	Description
class	Multistarter<T extends Copyable<T>>	This class is used for implementing multistart metaheuristics.
final class	ReoptimizableMultistarter<T extends Copyable<T>>	This class is used for implementing multistart metaheuristics, that can be restarted at previously found solutions.

Uses of TrackableSearch in org.cicirello.search.sa

Classes in org.cicirello.search.sa that implement TrackableSearch

Modifier and Type	Class	Description
final class	SimulatedAnnealing<T extends Copyable<T>>	This class is an implementation of the metaheuristic known as simulated annealing.

Uses of TrackableSearch in org.cicirello.search.ss

Classes in org.cicirello.search.ss that implement TrackableSearch

Modifier and Type	Class	Description
final class	AcceptanceBandSampling<T extends Copyable<T>>	The AcceptanceBandSampling class implements a form of stochastic sampling search that uses a constructive heuristic to guide the random decisions.
final class	HeuristicBiasedStochasticSampling<T extends Copyable<T>>	Heuristic Biased Stochastic Sampling (HBSS) is a form of stochastic sampling search that uses a constructive heuristic to bias the random decisions.
final class	HeuristicPermutationGenerator	This class generates solutions to permutation optimization problems using a constructive heuristic.
class	HeuristicSolutionGenerator<T extends Copyable<T>>	This class generates solutions to optimization problems using a constructive heuristic.
final class	IterativeSampling<T extends Copyable<T>>	Iterative sampling is the simplest possible form of a stochastic sampling search.
final class	ValueBiasedStochasticSampling<T extends Copyable<T>>	Value Biased Stochastic Sampling (VBSS) is a form of stochastic sampling search that uses a constructive heuristic to bias the random decisions.