Uses of Package
org.cicirello.search.operators

Package
Description
This package includes classes and interfaces directly related to implementing evolutionary algorithms.
This package includes classes and interfaces directly related to implementing hill climbers.
This package includes classes and interfaces for defining various operators required by simulated annealing and other metaheuristics, such as mutation operators, along with other related classes and interfaces.
This package includes classes that implement operators that create, mutate, etc, BitVectors.
This package includes classes that implement operators that create, mutate, etc, integer valued representations.
This package includes classes that implement mutation operators for permutations, and other operators required by simulated annealing and other related metaheuristics.
This package includes classes that implement operators that create, mutate, etc, the the inputs to functions with real-valued input parameters (represented with type double), such as is required to solve function optimization problems using simulated annealing or other metaheuristics.
Package of classes and interfaces related to representing computational problems, as well as classes implementing a variety of specific computational problems.
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.
  • Class
    Description
    Implement the CrossoverOperator interface to implement a crossover operator for use in evolutionary algorithms.
    Implement the Initializer interface to provide metaheuristics and other search algorithms with a way to generate initial candidate solutions to a problem.
    Implement the MutationOperator interface to implement a mutation operator for use in simulated annealing, genetic algorithms, and other evolutionary algorithms, and other metaheuristics, that require a way to generate random neighbors of a candidate solution.
    Implement the UndoableMutationOperator interface to implement a mutation operator for use in simulated annealing, and other metaheuristics, that require a way to generate random neighbors of a candidate solution, and which supports an undo method.
  • Class
    Description
    Implement the Initializer interface to provide metaheuristics and other search algorithms with a way to generate initial candidate solutions to a problem.
    Implement the IterableMutationOperator interface to define a mutation operator that enables iterating systematically over the neighbors of a candidate solution, like one would do in a hill climber.
  • Class
    Description
    Implement the CrossoverOperator interface to implement a crossover operator for use in evolutionary algorithms.
    A HybridCrossover enables using multiple crossover operators for the evolutionary algorithm, such that each time the HybridCrossover.cross(T, T) method is called, a randomly chosen crossover operator is applied to the candidate solution.
    A HybridMutation enables using multiple mutation operators for the search, such that each time the HybridMutation.mutate(T) method is called, a randomly chosen mutation operator is applied to the candidate solution.
    A HybridMutation enables using multiple mutation operators for the search, such that each time the HybridUndoableMutation.mutate(T) method is called, a randomly chosen mutation operator is applied to the candidate solution.
    This class implements the Initializer interface to provide metaheuristics and other search algorithms with a way to generate initial candidate solutions to a problem, that are themselves generated via a metaheuristic.
    Implement the Initializer interface to provide metaheuristics and other search algorithms with a way to generate initial candidate solutions to a problem.
    Implement the IterableMutationOperator interface to define a mutation operator that enables iterating systematically over the neighbors of a candidate solution, like one would do in a hill climber.
    Defines an interface for iterating over all of the mutants (i.e., neighbors) of a candidate solution to a problem.
    Implement the MutationOperator interface to implement a mutation operator for use in simulated annealing, genetic algorithms, and other evolutionary algorithms, and other metaheuristics, that require a way to generate random neighbors of a candidate solution.
    Implement the UndoableMutationOperator interface to implement a mutation operator for use in simulated annealing, and other metaheuristics, that require a way to generate random neighbors of a candidate solution, and which supports an undo method.
    A WeightedHybridCrossover enables using multiple crossover operators, such that each time the WeightedHybridCrossover.cross(T, T) method is called, a randomly chosen crossover operator is applied to the candidate solutions.
    A WeightedHybridMutation enables using multiple mutation operators for the search, such that each time the WeightedHybridMutation.mutate(T) method is called, a randomly chosen mutation operator is applied to the candidate solution.
    A WeightedHybridMutation enables using multiple mutation operators for the search, such that each time the WeightedHybridUndoableMutation.mutate(T) method is called, a randomly chosen mutation operator is applied to the candidate solution.
  • Class
    Description
    Implement the CrossoverOperator interface to implement a crossover operator for use in evolutionary algorithms.
    Implement the Initializer interface to provide metaheuristics and other search algorithms with a way to generate initial candidate solutions to a problem.
    Implement the IterableMutationOperator interface to define a mutation operator that enables iterating systematically over the neighbors of a candidate solution, like one would do in a hill climber.
    Defines an interface for iterating over all of the mutants (i.e., neighbors) of a candidate solution to a problem.
    Implement the MutationOperator interface to implement a mutation operator for use in simulated annealing, genetic algorithms, and other evolutionary algorithms, and other metaheuristics, that require a way to generate random neighbors of a candidate solution.
    Implement the UndoableMutationOperator interface to implement a mutation operator for use in simulated annealing, and other metaheuristics, that require a way to generate random neighbors of a candidate solution, and which supports an undo method.
  • Class
    Description
    Implement the CrossoverOperator interface to implement a crossover operator for use in evolutionary algorithms.
    Implement the Initializer interface to provide metaheuristics and other search algorithms with a way to generate initial candidate solutions to a problem.
    Implement the MutationOperator interface to implement a mutation operator for use in simulated annealing, genetic algorithms, and other evolutionary algorithms, and other metaheuristics, that require a way to generate random neighbors of a candidate solution.
    Implement the UndoableMutationOperator interface to implement a mutation operator for use in simulated annealing, and other metaheuristics, that require a way to generate random neighbors of a candidate solution, and which supports an undo method.
  • Class
    Description
    Implement the Initializer interface to provide metaheuristics and other search algorithms with a way to generate initial candidate solutions to a problem.
    Implement the IterableMutationOperator interface to define a mutation operator that enables iterating systematically over the neighbors of a candidate solution, like one would do in a hill climber.
    Defines an interface for iterating over all of the mutants (i.e., neighbors) of a candidate solution to a problem.
    Implement the MutationOperator interface to implement a mutation operator for use in simulated annealing, genetic algorithms, and other evolutionary algorithms, and other metaheuristics, that require a way to generate random neighbors of a candidate solution.
    Implement the UndoableMutationOperator interface to implement a mutation operator for use in simulated annealing, and other metaheuristics, that require a way to generate random neighbors of a candidate solution, and which supports an undo method.
  • Class
    Description
    Implement the CrossoverOperator interface to implement a crossover operator for use in evolutionary algorithms.
    Implement the Initializer interface to provide metaheuristics and other search algorithms with a way to generate initial candidate solutions to a problem.
    Implement the MutationOperator interface to implement a mutation operator for use in simulated annealing, genetic algorithms, and other evolutionary algorithms, and other metaheuristics, that require a way to generate random neighbors of a candidate solution.
    Implement the UndoableMutationOperator interface to implement a mutation operator for use in simulated annealing, and other metaheuristics, that require a way to generate random neighbors of a candidate solution, and which supports an undo method.
  • Class
    Description
    Implement the Initializer interface to provide metaheuristics and other search algorithms with a way to generate initial candidate solutions to a problem.
  • Class
    Description
    Implement the Initializer interface to provide metaheuristics and other search algorithms with a way to generate initial candidate solutions to a problem.
    Implement the UndoableMutationOperator interface to implement a mutation operator for use in simulated annealing, and other metaheuristics, that require a way to generate random neighbors of a candidate solution, and which supports an undo method.
  • Class
    Description
    Implement the Initializer interface to provide metaheuristics and other search algorithms with a way to generate initial candidate solutions to a problem.