Uses of Interface
org.cicirello.search.problems.Problem

Packages that use Problem 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.problems Package of classes and interfaces related to representing computational problems, as well as classes implementing a variety of specific computational problems.org.cicirello.search.problems.scheduling Package of classes and interfaces related to representing and solving scheduling problems, which includes implementations of constructive heuristics for scheduling problems.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 Problem in org.cicirello.search
Methods in org.cicirello.search that return Problem Modifier and Type Method Description Problem<T>
TrackableSearch. getProblem()
Gets a reference to the problem that this search is solving. 
Uses of Problem in org.cicirello.search.concurrent
Methods in org.cicirello.search.concurrent that return Problem Modifier and Type Method Description Problem<T>
ParallelMetaheuristic. getProblem()
Problem<T>
TimedParallelMultistarter. getProblem()

Uses of Problem in org.cicirello.search.problems
Subinterfaces of Problem in org.cicirello.search.problems Modifier and Type Interface Description interface
IntegerCostOptimizationProblem<T extends Copyable<T>>
The IntegerCostOptimizationProblem interface provides search algorithms with a way to interact with an instance of an optimization problem without the need to know the specifics of the problem (e.g., traveling salesperson, bin packing, etc).interface
OptimizationProblem<T extends Copyable<T>>
The OptimizationProblem interface provides search algorithms with a way to interact with an instance of an optimization problem without the need to know the specifics of the problem (e.g., realvalued function optimization, traveling salesperson, bin packing, etc).Classes in org.cicirello.search.problems that implement Problem Modifier and Type Class Description class
BoundMax
The BoundMax class is an implementation of a generalization of the wellknown OneMax problem, often used in benchmarking genetic algorithms and other metaheuristics.class
CostFunctionScaler<T extends Copyable<T>>
This is a wrapper class forOptimizationProblem
objects that enables scaling all cost values by a positive constant.class
ForresterEtAl2008
A continuous function with a single suboptimal local minimum, and a single global minimum, and a 0 derivative inflexion point, defined for inputs x in [0.0, 1.0].class
GramacyLee2012
A continuous function with a large number of local minimums, and a single global minimum, defined for input x in [0.5, 2.5].class
HollandRoyalRoad
Implementation of Holland's Royal Road problem, as described in the following paper:
Terry Jones.class
IntegerCostFunctionScaler<T extends Copyable<T>>
This is a wrapper class forIntegerCostOptimizationProblem
objects that enables scaling all cost values by a positive constant.class
Mix
This class implements Ackley's Mix problem, an artificial landscape that is a mix of the OneMax, TwoMax, Trap, and Plateau problems, which provides for a landscape that combines all of the properties of these benchmarking problems.class
OneMax
The OneMax class is an implementation of the wellknown OneMax problem, often used in benchmarking genetic algorithms and other metaheuristics.class
OneMaxAckley
The OneMaxAckley class is an implementation of the wellknown OneMax problem, often used in benchmarking genetic algorithms and other metaheuristics.class
PermutationInAHaystack
The Permutation in a Haystack is a family of optimization problems that can be parameterized to the various types of permutation problem (e.g., absolute versus relative positioning).class
Plateaus
This class implements Ackley's Plateaus problem, an artificial search landscape over the space of bitstrings that is characterized by large flat regions known as plateaus.class
PolynomialRootFinding
This class defines polynomial root finding as an optimization problem, enabling solving via simulated annealing or other metaheuristic optimization algorithms.class
Porcupine
This class implements the Porcupine landscape (Ackley, 1985), which is a very rugged search landscape, with an exponential number of local optima.class
RoyalRoad
Implementation of the Royal Road problem of Mitchell, Forrest, and Holland, both the variation with stepping stones and the one without.class
Trap
This class implements Ackley's Trap function, which defines a fitness landscape with a single global optima, and a single suboptimal local optima, such that most of the search landscape is within the attraction basin of the local optima.class
TwoMax
This class implements the benchmarking problem known as TwoMax.class
TwoMaxEqualPeaks
This class implements a variation of the benchmarking problem known as TwoMax. 
Uses of Problem in org.cicirello.search.problems.scheduling
Subinterfaces of Problem in org.cicirello.search.problems.scheduling Modifier and Type Interface Description interface
SingleMachineSchedulingProblem
Implement this interface to define a single machine scheduling problem.Classes in org.cicirello.search.problems.scheduling that implement Problem Modifier and Type Class Description class
MinimizeMakespan
Implements the common scheduling cost function known as makespan.class
MinimizeMaximumFlowtime
Implements the scheduling cost function known as maximum flowtime (which we want to minimize).class
MinimizeMaximumLateness
Implements the scheduling cost function known as maximum lateness, which we want to minimize.class
MinimizeMaximumTardiness
Implements the scheduling cost function known as maximum tardiness, which we want to minimize.class
WeightedEarlinessTardiness
Implements the scheduling cost function known as weighted earliness plus weighted tardiness.class
WeightedFlowtime
Implements the scheduling cost function known as weighted flowtime.class
WeightedLateness
Implements the scheduling cost function known as weighted lateness.class
WeightedNumberTardyJobs
Implements the scheduling cost function known as weighted number of tardy jobs, which we want to minimize.class
WeightedSquaredTardiness
Implements the scheduling cost function known as weighted squared tardiness.class
WeightedTardiness
Implements the scheduling cost function known as weighted tardiness. 
Uses of Problem in org.cicirello.search.restarts
Methods in org.cicirello.search.restarts that return Problem Modifier and Type Method Description Problem<T>
Multistarter. getProblem()

Uses of Problem in org.cicirello.search.sa
Methods in org.cicirello.search.sa that return Problem Modifier and Type Method Description Problem<T>
SimulatedAnnealing. getProblem()

Uses of Problem in org.cicirello.search.ss
Methods in org.cicirello.search.ss that return Problem Modifier and Type Method Description Problem<T>
ConstructiveHeuristic. getProblem()
Gets a reference to the instance of the optimization problem that is the subject of this heuristic.Problem<T>
HeuristicSolutionGenerator. getProblem()
Problem<T>
HybridConstructiveHeuristic. getProblem()
