Uses of Interface
org.cicirello.search.ReoptimizableMetaheuristic

Packages that use ReoptimizableMetaheuristic

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.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.

Uses of ReoptimizableMetaheuristic in org.cicirello.search

Subinterfaces of ReoptimizableMetaheuristic in org.cicirello.search

Modifier and Type	Interface	Description
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.

Methods in org.cicirello.search that return ReoptimizableMetaheuristic

Modifier and Type	Method	Description
ReoptimizableMetaheuristic<T>	ReoptimizableMetaheuristic.split()

Uses of ReoptimizableMetaheuristic in org.cicirello.search.concurrent

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

Modifier and Type	Class	Description
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.
final class	TimedParallelReoptimizableMultistarter<T extends Copyable<T>>	This class is used for implementing parallel multistart metaheuristics.

Constructors in org.cicirello.search.concurrent with parameters of type ReoptimizableMetaheuristic

Modifier	Constructor	Description
	ParallelReoptimizableMetaheuristic(ReoptimizableMetaheuristic<T> search, int numThreads)	Constructs a parallel metaheuristic that executes multiple identical copies of a metaheuristic in parallel across multiple threads.
	ParallelReoptimizableMultistarter(ReoptimizableMetaheuristic<T> search, int runLength, int numThreads)	Constructs a parallel multistart metaheuristic that executes multiple runs of a specified metaheuristic in parallel across multiple threads.
	ParallelReoptimizableMultistarter(ReoptimizableMetaheuristic<T> search, Collection<? extends RestartSchedule> schedules)	Constructs a parallel multistart metaheuristic that executes multiple runs of a specified metaheuristic in parallel across multiple threads.
	ParallelReoptimizableMultistarter(ReoptimizableMetaheuristic<T> search, RestartSchedule r, int numThreads)	Constructs a parallel multistart metaheuristic that executes multiple runs of a specified metaheuristic in parallel across multiple threads.
	TimedParallelReoptimizableMultistarter(ReoptimizableMetaheuristic<T> search, int runLength, int numThreads)	Constructs a parallel multistart metaheuristic that executes multiple runs of a specified metaheuristic in parallel across multiple threads.
	TimedParallelReoptimizableMultistarter(ReoptimizableMetaheuristic<T> search, Collection<? extends RestartSchedule> schedules)	Constructs a parallel multistart metaheuristic that executes multiple runs of a specified metaheuristic in parallel across multiple threads.
	TimedParallelReoptimizableMultistarter(ReoptimizableMetaheuristic<T> search, RestartSchedule r, int numThreads)	Constructs a parallel multistart metaheuristic that executes multiple runs of a specified metaheuristic in parallel across multiple threads.

Constructor parameters in org.cicirello.search.concurrent with type arguments of type ReoptimizableMetaheuristic

Modifier	Constructor	Description
	ParallelReoptimizableMetaheuristic(Collection<? extends ReoptimizableMetaheuristic<T>> searches)	Constructs a parallel metaheuristic that executes multiple metaheuristics in parallel across multiple threads.
	ParallelReoptimizableMultistarter(Collection<? extends ReoptimizableMetaheuristic<T>> searches, int runLength)	Constructs a parallel multistart metaheuristic that executes multiple runs of a set of specified metaheuristics in parallel across multiple threads.
	ParallelReoptimizableMultistarter(Collection<? extends ReoptimizableMetaheuristic<T>> searches, Collection<? extends RestartSchedule> schedules)	Constructs a parallel multistart metaheuristic that executes multiple runs of a set of specified metaheuristics in parallel across multiple threads.
	TimedParallelReoptimizableMultistarter(Collection<? extends ReoptimizableMetaheuristic<T>> searches, int runLength)	Constructs a parallel multistart metaheuristic that executes multiple runs of a set of specified metaheuristics in parallel across multiple threads.
	TimedParallelReoptimizableMultistarter(Collection<? extends ReoptimizableMetaheuristic<T>> searches, Collection<? extends RestartSchedule> schedules)	Constructs a parallel multistart metaheuristic that executes multiple runs of a set of specified metaheuristics in parallel across multiple threads.

Uses of ReoptimizableMetaheuristic in org.cicirello.search.evo

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

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	NaiveGenerationalEvolutionaryAlgorithm<T extends Copyable<T>>	Deprecated. IMPORTANT: This class is being introduced temporarily in support of research experiments.
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 ReoptimizableMetaheuristic in org.cicirello.search.restarts

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

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

Constructors in org.cicirello.search.restarts with parameters of type ReoptimizableMetaheuristic

Modifier	Constructor	Description
	ReoptimizableMultistarter(ReoptimizableMetaheuristic<T> search, int runLength)	Constructs a multistart metaheuristic that executes multiple runs of a specified metaheuristic, whose runs are all the same in length.
	ReoptimizableMultistarter(ReoptimizableMetaheuristic<T> search, RestartSchedule r)	Constructs a multistart metaheuristic that executes multiple runs of a specified metaheuristic, whose run lengths follow a specified schedule.

Uses of ReoptimizableMetaheuristic in org.cicirello.search.sa

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

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