Documentation of 'org.jgap.impl.salesman.Salesman' Java class
Salesman
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org.jgap.impl.salesman

Class Salesman

  • All Implemented Interfaces:
    java.io.Serializable

    public abstract class Salesman
extends java.lang.Object
implements java.io.Serializable
    The class solves the travelling salesman problem. The traveling salesman problem, or TSP for short, is this: given a finite number of 'cities' along with the cost of travel between each pair of them, find the cheapest way of visiting all the cities and returning to your starting point.)
    Since:
    2.0
    See Also:
    • J. Grefenstette, R. Gopal, R. Rosmaita, and D. Gucht. Genetic algorithms for the traveling salesman problem. In Proceedings of the Second International Conference on Genetic Algorithms. Lawrence Eribaum Associates, Mahwah, NJ, 1985.
    • Sushil J. Louis & Gong Li (explanatory material)
    • TPS web site
    , Serialized Form

    • Constructor Detail

      • Salesman

        public Salesman()

    • Method Detail

      • distance

        public abstract double distance(Gene a_from,
                                Gene a_to)
        Override this method to compute the distance between "cities", indicated by these two given genes. The algorithm is not dependent on the used type of genes.
        Parameters:
        a_from - first gene, representing a city
        a_to - second gene, representing a city
        Returns:
        the distance between two cities represented as genes
        Since:
        2.0

      • createSampleChromosome

        public abstract IChromosome createSampleChromosome(java.lang.Object a_initial_data)
        Override this method to create a single sample chromosome, representing a list of "cities". Each gene corresponds a single "city" and can appear only once. By default, the first gene corresponds a "city" where the salesman starts the journey. It never changes its position. This can be changed by setting other start offset with setStartOffset( ). Other genes will be shuffled to create the initial random population.
        Parameters:
        a_initial_data - the same object as was passed to findOptimalPath. It can be used to specify the task more precisely if the class is used for solving multiple tasks
        Returns:
        a sample chromosome
        Since:
        2.0

      • createFitnessFunction

        public FitnessFunction createFitnessFunction(java.lang.Object a_initial_data)
        Return the fitness function to use.
        Parameters:
        a_initial_data - the same object as was passed to findOptimalPath. It can be used to specify the task more precisely if the class is used for solving multiple tasks
        Returns:
        an applicable fitness function
        Since:
        2.0

      • createConfiguration

        public Configuration createConfiguration(java.lang.Object a_initial_data)
                                  throws InvalidConfigurationException
        Create a configuration. The configuration should not contain operators for odrinary crossover and mutations, as they make chromosoms invalid in this task. The special operators SwappingMutationOperator and GreedyCrossober should be used instead.
        Parameters:
        a_initial_data - the same object as was passed to findOptimalPath. It can be used to specify the task more precisely if the class is used for solving multiple tasks
        Returns:
        created configuration
        Throws:
        InvalidConfigurationException
        Since:
        2.0

      • getMaxEvolution

        public int getMaxEvolution()
        Returns:
        maximal number of iterations for population to evolve
        Since:
        2.0

      • setMaxEvolution

        public void setMaxEvolution(int a_maxEvolution)
        Set the maximal number of iterations for population to evolve (default 512).
        Parameters:
        a_maxEvolution - sic
        Since:
        2.0

      • getPopulationSize

        public int getPopulationSize()
        Returns:
        population size for this solution
        Since:
        2.0

      • setPopulationSize

        public void setPopulationSize(int a_populationSize)
        Set an population size for this solution (default 512)
        Parameters:
        a_populationSize - sic
        Since:
        2.0

      • findOptimalPath

        public IChromosome findOptimalPath(java.lang.Object a_initial_data)
                            throws java.lang.Exception
        Executes the genetic algorithm to determine the optimal path between the cities.
        Parameters:
        a_initial_data - can be a record with fields, specifying the task more precisely if the class is used to solve multiple tasks. It is passed to createFitnessFunction, createSampleChromosome and createConfiguration
        Returns:
        chromosome representing the optimal path between cities
        Throws:
        java.lang.Exception
        Since:
        2.0

      • setStartOffset

        public void setStartOffset(int a_offset)
        Sets a number of genes at the start of chromosome, that are excluded from the swapping. In the Salesman task, the first city in the list should (where the salesman leaves from) probably should not change as it is part of the list. The default value is 1.
        Parameters:
        a_offset - start offset for chromosome
        Since:
        2.0

      • getStartOffset

        public int getStartOffset()
        Gets a number of genes at the start of chromosome, that are excluded from the swapping. In the Salesman task, the first city in the list should (where the salesman leaves from) probably should not change as it is part of the list. The default value is 1.
        Returns:
        start offset for chromosome
        Since:
        2.0
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