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

Packages that use Problem
Package
Description
This package includes classes and interfaces related to implementing metaheuristic search algorithms in general, rather than specific to a particular metaheuristic.
This package includes multithreaded search implementations, as well as classes and interfaces related to implementing multithreaded metaheuristics.
This package includes classes and interfaces directly related to implementing evolutionary algorithms.
Package of classes and interfaces related to representing computational problems, as well as classes implementing a variety of specific computational problems.
Classes and interfaces related to the Bin Packing.
Package of classes and interfaces related to representing and solving scheduling problems, which includes implementations of constructive heuristics for scheduling problems.
Classes and interfaces related to the Traveling Salesperson Problem (TSP).
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).
This package includes classes and interfaces directly related to implementing simulated annealing.
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
    TrackableSearch.getProblem()
    Gets a reference to the problem that this search is solving.
  • Uses of Problem in org.cicirello.search.concurrent

    Modifier and Type
    Method
    Description
    final Problem<T>
    ParallelMetaheuristic.getProblem()
     
    final Problem<T>
    TimedParallelMultistarter.getProblem()
     
  • Uses of Problem in org.cicirello.search.evo

    Methods in org.cicirello.search.evo that return Problem
    Modifier and Type
    Method
    Description
    FitnessFunction.getProblem()
    Gets a reference to the problem that this fitness function is for.
    InverseCostFitnessFunction.getProblem()
     
    final Problem<T>
    OnePlusOneEvolutionaryAlgorithm.getProblem()
     
  • Uses of Problem in org.cicirello.search.problems

    Modifier and Type
    Interface
    Description
    interface 
    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 
    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., real-valued function optimization, traveling salesperson, bin packing, etc).
    Classes in org.cicirello.search.problems that implement Problem
    Modifier and Type
    Class
    Description
    final class 
    The BoundMax class is an implementation of a generalization of the well-known OneMax problem, often used in benchmarking genetic algorithms and other metaheuristics.
    final class 
    This is a wrapper class for OptimizationProblem objects that enables scaling all cost values by a positive constant.
    final class 
    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].
    final class 
    A continuous function with a large number of local minimums, and a single global minimum, defined for input x in [0.5, 2.5].
    final class 
    Implementation of Holland's Royal Road problem, as described in the following paper:
    Terry Jones.
    final class 
    This is a wrapper class for IntegerCostOptimizationProblem objects that enables scaling all cost values by a positive constant.
    final class 
    This class is an implementation of the Largest Common Subgraph problem, an NP-Hard combinatorial optimization problem.
    final class 
    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.
    final class 
    The OneMax class is an implementation of the well-known OneMax problem, often used in benchmarking genetic algorithms and other metaheuristics.
    final class 
    The OneMaxAckley class is an implementation of the well-known OneMax problem, often used in benchmarking genetic algorithms and other metaheuristics.
    final class 
    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).
    static final class 
    This class implements a mapping between Permutation problems and BitVector problems, where cost values are of type double.
    static final class 
    This class implements a mapping between Permutation problems and BitVector problems, where cost values are of type int.
    final class 
    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.
    final class 
    This class defines polynomial root finding as an optimization problem, enabling solving via simulated annealing or other metaheuristic optimization algorithms.
    final class 
    This class implements the Porcupine landscape (Ackley, 1985), which is a very rugged search landscape, with an exponential number of local optima.
    final class 
    This class is an implementation of the Quadratic Assignment Problem (QAP), an NP-Hard optimization problem.
    final class 
    Implementation of the Royal Road problem of Mitchell, Forrest, and Holland, both the variation with stepping stones and the one without.
    final class 
    This class implements Ackley's Trap function, which defines a fitness landscape with a single global optima, and a single sub-optimal local optima, such that most of the search landscape is within the attraction basin of the local optima.
    final class 
    This class implements the benchmarking problem known as TwoMax.
    final class 
    This class implements a variation of the benchmarking problem known as TwoMax.
  • Uses of Problem in org.cicirello.search.problems.binpack

    Modifier and Type
    Class
    Description
    class 
    This class, and its nested classes, implements the Bin Packing problem.
    static final class 
    Generates instances of the Bin Packing problem where the optimal solution is comprised of all full triplet bins (each bin in optimal solution has exactly 3 items that fills the bin to capacity).
    static final class 
    Generates instances of the Bin Packing problem with item sizes that are generated uniformly at random.
  • Uses of Problem in org.cicirello.search.problems.scheduling

    Modifier and Type
    Interface
    Description
    interface 
    Implement this interface to define a single machine scheduling problem.
    Modifier and Type
    Class
    Description
    final class 
    Implements the common scheduling cost function known as makespan.
    final class 
    Implements the scheduling cost function known as maximum flowtime (which we want to minimize).
    final class 
    Implements the scheduling cost function known as maximum lateness, which we want to minimize.
    final class 
    Implements the scheduling cost function known as maximum tardiness, which we want to minimize.
    final class 
    Implements the scheduling cost function known as weighted earliness plus weighted tardiness.
    final class 
    Implements the scheduling cost function known as weighted flowtime.
    final class 
    Implements the scheduling cost function known as weighted lateness.
    final class 
    Implements the scheduling cost function known as weighted number of tardy jobs, which we want to minimize.
    final class 
    Implements the scheduling cost function known as weighted squared tardiness.
    final class 
    Implements the scheduling cost function known as weighted tardiness.
  • Uses of Problem in org.cicirello.search.problems.tsp

    Classes in org.cicirello.search.problems.tsp that implement Problem
    Modifier and Type
    Class
    Description
    class 
    This class serves as an abstract base class for the various classes that implement variations of the Traveling Salesperson Problem provided by the library.
    class 
    This class and its nested classes implement the Traveling Salesperson Problem (TSP), and its variant, the Asymmetric Traveling Salesperson Problem (ATSP), by generating a random distance matrix.
    static final class 
    This class implements the Traveling Salesperson Problem (TSP), and its variant, the Asymmetric Traveling Salesperson Problem (ATSP), by generating a random distance matrix, with floating-point cost edges.
    static final class 
    This class implements the Traveling Salesperson Problem (TSP), and its variant, the Asymmetric Traveling Salesperson Problem (ATSP), by generating a random distance matrix, with integer cost edges.
    class 
    This class and its nested classes implement the Traveling Salesperson Problem (TSP), such that cities are 2D points, and edge costs is the distance between them.
    static final class 
    Cost function for the Traveling Salesperson Problem (TSP), where edge costs are floating-point valued.
    static final class 
    Cost function for the Traveling Salesperson Problem (TSP), where edge costs are floating-point valued, and where all edge costs between pairs of cities are precomputed.
    static final class 
    Cost function for the Traveling Salesperson Problem (TSP), where edge costs are integer valued.
    static final class 
    Cost function for the Traveling Salesperson Problem (TSP), where edge costs are integer valued, and where all edge costs between pairs of cities are precomputed.
    Modifier and Type
    Method
    Description
    NearestCityHeuristic.getProblem()
     
    NearestCityPairHeuristic.getProblem()
     
  • Uses of Problem in org.cicirello.search.restarts

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