From 4f26361eb08aa09334d1eea0a124110c20d809ce Mon Sep 17 00:00:00 2001 From: Eduardo Pedroni Date: Sat, 18 Oct 2014 15:21:45 +0200 Subject: Moved javadocs to a different repository for hosting, updated README --- doc/jcgp/backend/modules/Module.html | 288 ------------- doc/jcgp/backend/modules/class-use/Module.html | 292 ------------- .../backend/modules/es/EvolutionaryStrategy.html | 290 ------------- doc/jcgp/backend/modules/es/MuPlusLambda.html | 330 --------------- .../backend/modules/es/TournamentSelection.html | 333 --------------- .../modules/es/class-use/EvolutionaryStrategy.html | 204 --------- .../backend/modules/es/class-use/MuPlusLambda.html | 124 ------ .../modules/es/class-use/TournamentSelection.html | 124 ------ doc/jcgp/backend/modules/es/package-frame.html | 22 - doc/jcgp/backend/modules/es/package-summary.html | 162 ------- doc/jcgp/backend/modules/es/package-tree.html | 146 ------- doc/jcgp/backend/modules/es/package-use.html | 180 -------- 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jcgp.backend.modules
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Class Module

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    Direct Known Subclasses:
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    EvolutionaryStrategy, Mutator, Problem
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    public abstract class Module
-extends java.lang.Object
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    This class defines the expected behaviour of a module. Generally, modules - are entities which contain parameters; these can be retrieved using - getLocalParameters(). GUIs should make use of this getter - to display visual parameter controls to users. Subclasses don't have direct - access to the list; instead they must use registerParameter() (ideally - in the constructor) to make sure the parameters are returned. -
    - In addition, implementations of Module should specify a module name - in their constructor using setName(). If a name is not provided, - the simple name of the class will be used. -
    - All modules, by definition, contain a reference to the experiment's resources, which - must be passed at construction. The resources should be accessed with getResources().
    -
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    Author:
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    Eduardo Pedroni
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    Parameter
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      Method Summary

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      Modifier and TypeMethod and Description
      java.util.ArrayList<Parameter<?>>getLocalParameters() -
      This method is used by the GUI in order to build visual - representations of all parameters used by the module.
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      java.lang.StringtoString() 
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        public java.util.ArrayList<Parameter<?>> getLocalParameters()
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        This method is used by the GUI in order to build visual - representations of all parameters used by the module. - Therefore, any parameters returned here will be displayed - visually.
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        a list of generic parameters exposed by the module.
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jcgp.backend.modules.es
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Class EvolutionaryStrategy

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    public abstract class EvolutionaryStrategy
-extends Module
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    This class specifies the characteristics of an evolutionary strategy. The evolutionary - strategy's job is to generate the next population of solutions. In JCGP this is done by modifying - the provided population object rather than creating a new one. -

    - A typical implementation of EvolutionaryStratey iterates through the chromosomes - in the population and selects the individual(s) to be promoted. It then uses - mutator.mutate() to generically mutate the promoted individual(s). Parameter-dependent - strategies can be implemented by accessing the parameters via the resources - argument. -

    - Parameters may be specified to control the implemented strategy. Any parameters - registered with registerParameters() should be displayed by the user interface, - if it is being used. See Module for more information. -

    - It is advisable to use Resources.reportln() and Resources.report() - to print any relevant information. Note that reportln() and report() are affected - by the report interval base parameter. Use Resources.println() and - Resources.print() to print information regardless of the current generation. - See Resources for more information.
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    Author:
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    Eduardo Pedroni
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    See Also:
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    Module
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      Method Summary

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      Modifier and TypeMethod and Description
      abstract voidevolve(Population population, - Mutator mutator) -
      Performs the selection algorithm and uses the mutator to create - the next generation of solutions.
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        evolve

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        public abstract void evolve(Population population,
-                            Mutator mutator)
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        Performs the selection algorithm and uses the mutator to create - the next generation of solutions.
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        population - the population to evolve.
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jcgp.backend.modules.es
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Class MuPlusLambda

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    (μ + λ)-ES -

    - This strategy selects the μ fittest chromosomes from the population. - The promoted individuals are copied into the new population and mutated - λ times, but also carried forward unchanged. The total population size - is μ + λ. -

    - Two integer parameters are used to control this strategy: parents - and offspring. They are constrained in that they must always add up to - the population size, and must never be smaller than 1. -
    - One additional parameter, report, controls whether a detailed log of the - algorithm's operation is to be printed or not. Reports respect the report - interval base parameter.
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    Author:
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    Eduardo Pedroni
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      Method Summary

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        Description copied from class: EvolutionaryStrategy
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        Performs the selection algorithm and uses the mutator to create - the next generation of solutions.
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Class TournamentSelection

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    Tournament selection -

    - This strategy generates a new population by selecting a specified number - of chromosomes from the original population and selecting the fittest out - of the isolated subset (the tournament). The selected individual is mutated - using the specified mutator. This process is repeated until the new population - is complete. -

    - One integer parameter is used to control this strategy: tournament - size. This must always be greater than 0 and smaller than or equal to the - population size. Setting it to equal population size results in the same - chromosome being selected for every tournament, and setting it to 1 leads - to an effectively random search. -
    - One additional parameter, report, controls whether a detailed log of the - algorithm's operation is to be printed or not. Reports respect the report - interval base parameter.
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        Description copied from class: EvolutionaryStrategy
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        Performs the selection algorithm and uses the mutator to create - the next generation of solutions.
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        Specified by:
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        evolve in class EvolutionaryStrategy
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        population - the population to evolve.
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jcgp.backend.modules.es.MuPlusLambda

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- - - diff --git a/doc/jcgp/backend/modules/mutator/FixedPointMutator.html b/doc/jcgp/backend/modules/mutator/FixedPointMutator.html deleted file mode 100644 index 2154503..0000000 --- a/doc/jcgp/backend/modules/mutator/FixedPointMutator.html +++ /dev/null @@ -1,284 +0,0 @@ - - - - - -FixedPointMutator - - - - - - - - -
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jcgp.backend.modules.mutator
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Class FixedPointMutator

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    public class FixedPointMutator
-extends PointMutator
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    Fixed point mutator -

    - This operator uses the point mutator - algorithm to mutate a user-defined fixed - number of genes.
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    Author:
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    Eduardo Pedroni
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      FixedPointMutator(Resources resources) -
      Creates a new instance of FixedPointMutator.
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jcgp.backend.modules.mutator
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Class Mutator

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    PointMutator, ProbabilisticMutator
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    public abstract class Mutator
-extends Module
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    This class specifies the basic characteristics of a mutation operator. Its job is - to modify the connections and functions of the chromosome according to the operator's - parameters. -

    - Parameters may be specified to control the implemented mutation. Any parameters - registered with registerParameters() should be displayed by the user interface, - if it is being used. See Module for more information. -

    - It is advisable to use Resources.reportln() and Resources.report() - to print any relevant information. Note that reportln() and report() are affected - by the report interval base parameter. Use Resources.println() and - Resources.print() to print information regardless of the current generation. - See Resources for more information.
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    Author:
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    Eduardo Pedroni
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      Method Summary

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      Modifier and TypeMethod and Description
      abstract voidmutate(Chromosome chromosome) -
      Applies mutations to the specified chromosome according - to the parameter values.
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        mutate

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        public abstract void mutate(Chromosome chromosome)
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        Applies mutations to the specified chromosome according - to the parameter values.
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        chromosome - the chromosome to mutate.
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jcgp.backend.modules.mutator
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Class PercentPointMutator

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    public class PercentPointMutator
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    Percent point mutator -

    - This operator calculates how many genes to mutate based on the mutation rate - parameter. The total number of genes is computed from the number of nodes, - the arity and the number of outputs. It then uses the point mutation - algorithm to perform the required number of mutations.
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      Constructor and Description
      PercentPointMutator(Resources resources) -
      Creates a new instance of PercentPointMutator.
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        Creates a new instance of PercentPointMutator.
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jcgp.backend.modules.mutator
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Class PointMutator

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    public abstract class PointMutator
-extends Mutator
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    Point mutator -

    - In point mutation, a number of random genes - is picked and mutated until all required - mutations have been performed. The actual number - of genes to be mutated can be defined in any - arbitrary way.
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      Method Summary

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      Modifier and TypeMethod and Description
      voidmutate(Chromosome chromosome) -
      Applies mutations to the specified chromosome according - to the parameter values.
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        public void mutate(Chromosome chromosome)
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        Description copied from class: Mutator
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        Applies mutations to the specified chromosome according - to the parameter values.
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        mutate in class Mutator
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        chromosome - the chromosome to mutate.
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jcgp.backend.modules.mutator
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Class ProbabilisticMutator

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-extends Mutator
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    Probabilistic mutator -

    - This operator iterates through every mutable gene in the chromosome and - decides whether to mutate each of them individually. - The decision is made based on the difference between the mutation probability - and a randomly generated double between 0 and 100.
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    Eduardo Pedroni
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      Method Summary

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      Modifier and TypeMethod and Description
      voidmutate(Chromosome chromosome) -
      Applies mutations to the specified chromosome according - to the parameter values.
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        mutate

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        public void mutate(Chromosome chromosome)
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        Description copied from class: Mutator
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        Applies mutations to the specified chromosome according - to the parameter values.
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        Specified by:
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        mutate in class Mutator
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        chromosome - the chromosome to mutate.
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jcgp.backend.modules.mutator.PercentPointMutator

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- - - diff --git a/doc/jcgp/backend/modules/problem/BestFitness.html b/doc/jcgp/backend/modules/problem/BestFitness.html deleted file mode 100644 index 4d19d5c..0000000 --- a/doc/jcgp/backend/modules/problem/BestFitness.html +++ /dev/null @@ -1,352 +0,0 @@ - - - - - -BestFitness - - - - - - - - -
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jcgp.backend.modules.problem
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Enum BestFitness

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    All Implemented Interfaces:
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    java.io.Serializable, java.lang.Comparable<BestFitness>
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    public enum BestFitness
-extends java.lang.Enum<BestFitness>
    -
    Enum type to allow problems to indicate their fitness - orientation. -

    - BestFitness.HIGH means high fitness values are - better than low. Conversely, BestFitness.LOW - signals that low fitness values indicate better fitness - than high values.
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    Author:
    -
    Eduardo Pedroni
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      Enum Constant Summary

      - - - - - - - - - - - -
      Enum Constants 
      Enum Constant and Description
      HIGH 
      LOW 
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      Method Summary

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      All Methods Static Methods Concrete Methods 
      Modifier and TypeMethod and Description
      static BestFitnessvalueOf(java.lang.String name) -
      Returns the enum constant of this type with the specified name.
      -
      static BestFitness[]values() -
      Returns an array containing the constants of this enum type, in -the order they are declared.
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        Methods inherited from class java.lang.Enum

        -compareTo, equals, getDeclaringClass, hashCode, name, ordinal, toString, valueOf
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        Methods inherited from class java.lang.Object

        -getClass, notify, notifyAll, wait, wait, wait
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      Method Detail

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        values

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        public static BestFitness[] values()
        -
        Returns an array containing the constants of this enum type, in -the order they are declared. This method may be used to iterate -over the constants as follows: -
        -for (BestFitness c : BestFitness.values())
-    System.out.println(c);
-
        -
        -
        Returns:
        -
        an array containing the constants of this enum type, in the order they are declared
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      - - - -
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        valueOf

        -
        public static BestFitness valueOf(java.lang.String name)
        -
        Returns the enum constant of this type with the specified name. -The string must match exactly an identifier used to declare an -enum constant in this type. (Extraneous whitespace characters are -not permitted.)
        -
        -
        Parameters:
        -
        name - the name of the enum constant to be returned.
        -
        Returns:
        -
        the enum constant with the specified name
        -
        Throws:
        -
        java.lang.IllegalArgumentException - if this enum type has no constant with the specified name
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        java.lang.NullPointerException - if the argument is null
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- - - diff --git a/doc/jcgp/backend/modules/problem/DigitalCircuitProblem.html b/doc/jcgp/backend/modules/problem/DigitalCircuitProblem.html deleted file mode 100644 index 2eab7fe..0000000 --- a/doc/jcgp/backend/modules/problem/DigitalCircuitProblem.html +++ /dev/null @@ -1,423 +0,0 @@ - - - - - -DigitalCircuitProblem - - - - - - - - -
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jcgp.backend.modules.problem
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Class DigitalCircuitProblem

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    public class DigitalCircuitProblem
-extends TestCaseProblem<UnsignedInteger>
    -
    Digital circuit problem -

    - Using this problem type, digital logic circuits can be evolved. - parseData() must be used to load the desired circuit - truth table in the standard CGP .plu format.
    -
    -
    Author:
    -
    Eduardo Pedroni
    -
    See Also:
    -
    DigitalCircuitFunctions
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      Constructor Detail

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        DigitalCircuitProblem

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        public DigitalCircuitProblem(Resources resources)
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        Construct a new instance of DigitalCircuitProblem.
        -
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        Parameters:
        -
        resources - a reference to the experiment's resources.
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      Method Detail

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        evaluate

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        public void evaluate(Population population)
        -
        Description copied from class: Problem
        -
        The most important method of the problem type. This is called once - per generation, when the new population has been generated. -

        - The basic functionality of this method is to loop through all chromosomes - in the population and decode them according to the problem type. The - fitness of each chromosome is then calculated using the problem data - or otherwise (subjective problem types such as art generation might - leave fitness evaluations up to the user) and assigned to the appropriate - chromosome. -

        - In addition, realisations of this method should update the value of - bestFitness as appropriate, since the value of this parameter is displayed - if a GUI is in use.
        -
        -
        Specified by:
        -
        evaluate in class Problem
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        Parameters:
        -
        population - the population to be evaluated.
        -
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        parseTestCase

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        public TestCaseProblem.TestCase<UnsignedInteger> parseTestCase(java.lang.String[] inputs,
-                                                               java.lang.String[] outputs)
        -
        Description copied from class: TestCaseProblem
        -
        This method is used internally by addTestCase() in order - to appropriately parse strings into the right data type for the - test cases. Since the data type is problem-dependent, subclasses must - implement this method. This method must return a built TestCase - object from the arguments given.
        -
        -
        Parameters:
        -
        inputs - the inputs represented as strings.
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        outputs - the outputs represented as strings.
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        Returns:
        -
        the parsed test case.
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        hasPerfectSolution

        -
        public int hasPerfectSolution(Population population)
        -
        Description copied from class: Problem
        -
        Used to assert whether a given population contains a perfect solution - to the problem. It is up to the problem to define what qualifies - a perfect solution, as some problems (subject ones such as music and - art evolution, for example) might not have perfect solutions at all.
        -
        -
        Specified by:
        -
        hasPerfectSolution in class Problem
        -
        Parameters:
        -
        population - the population to search through for a perfect chromosome.
        -
        Returns:
        -
        the perfect solution index, if one exits, -1 if no perfect solution was found.
        -
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jcgp.backend.modules.problem
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Class Problem

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    Direct Known Subclasses:
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    TestCaseProblem
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    public abstract class Problem
-extends Module
    -
    Defines the general behaviour of a CGP problem. The primary function of Problem - is to evaluate a population and assign a fitness value to each chromosome. -
    - Problems are free to define whether better fitness means a higher or lower fitness value. - In some problem types, it is more convenient to treat fitness 0 as the best possible value. - This can be done by changing the fitness orientation to BestFitness.HIGH or BestFitness.LOW as appropriate. - Fitness orientation is set to high by default. -

    - When extending this class, the constructor should call a few methods in order to - properly construct the problem type: setFunctionSet(), setFileExtension() and setFitnessOrientation(), - with the respective arguments. As with all subclasses of Module, setName() and - registerParameters() should be used where appropriate as well. -

    - It is advisable to use Resources.reportln() and Resources.report() - to print any relevant information. Note that reportln() and report() are affected - by the report interval base parameter. Use Resources.println() and - Resources.print() to print information regardless of the current generation. - See Resources for more information.
    -
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    Author:
    -
    Eduardo Pedroni
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    See Also:
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    Module
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      Method Detail

      - - - -
        -
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        evaluate

        -
        public abstract void evaluate(Population population)
        -
        The most important method of the problem type. This is called once - per generation, when the new population has been generated. -

        - The basic functionality of this method is to loop through all chromosomes - in the population and decode them according to the problem type. The - fitness of each chromosome is then calculated using the problem data - or otherwise (subjective problem types such as art generation might - leave fitness evaluations up to the user) and assigned to the appropriate - chromosome. -

        - In addition, realisations of this method should update the value of - bestFitness as appropriate, since the value of this parameter is displayed - if a GUI is in use.
        -
        -
        Parameters:
        -
        population - the population to be evaluated.
        -
        -
        • -
      - - - -
        -
      • -

        hasPerfectSolution

        -
        public abstract int hasPerfectSolution(Population population)
        -
        Used to assert whether a given population contains a perfect solution - to the problem. It is up to the problem to define what qualifies - a perfect solution, as some problems (subject ones such as music and - art evolution, for example) might not have perfect solutions at all.
        -
        -
        Parameters:
        -
        population - the population to search through for a perfect chromosome.
        -
        Returns:
        -
        the perfect solution index, if one exits, -1 if no perfect solution was found.
        -
        -
        • -
      - - - -
        -
      • -

        hasImprovement

        -
        public abstract int hasImprovement(Population population)
        -
        Used to assert whether a given population has a chromosome that is an improvement over - the current best chromosome. A typical implementation of this method - will simply compare chromosome fitness values, though the problem type - is free to implement this in any way.
        -
        -
        Parameters:
        -
        population - the population potentially containing a fitter chromosome.
        -
        Returns:
        -
        the index of the first chromosome in the population that is an improvement, -1 if none is found.
        -
        -
        • -
      - - - -
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      • -

        parseProblemData

        -
        public abstract void parseProblemData(java.io.File file,
-                                      ModifiableResources resources)
        -
        Parses the specified file and uses the parsed data to - set up the problem type instance appropriately. Any necessary - resource changes can be performed using the provided ModifiableResources - instance. -

        - In addition, realisations of this method should update the value of - maxFitness where appropriate, as this may be displayed to the user - if a GUI is in use.
        -
        -
        Parameters:
        -
        file - the data file to parse.
        -
        resources - a modifiable reference to the experiment's resources.
        -
        -
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        getFunctionSet

        -
        public FunctionSet getFunctionSet()
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        -
        Returns:
        -
        the FunctionSet object used by this problem type.
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        -
        • -
      - - - -
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        getFileExtension

        -
        public java.lang.String getFileExtension()
        -
        -
        Returns:
        -
        the file extension accepted by this problem type for problem data files.
        -
        -
        • -
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        getFitnessOrientation

        -
        public BestFitness getFitnessOrientation()
        -
        -
        Returns:
        -
        the fitness orientation of this particular problem.
        -
        -
        • -
      - - - -
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      • -

        getBestFitness

        -
        public double getBestFitness()
        -
        -
        Returns:
        -
        the current best fitness, in other words, the fitness - value of the fittest chromosome in the current generation.
        -
        -
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        reset

        -
        public void reset()
        -
        Resets the bestFitness parameter.
        -
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- - - diff --git a/doc/jcgp/backend/modules/problem/SymbolicRegressionProblem.html b/doc/jcgp/backend/modules/problem/SymbolicRegressionProblem.html deleted file mode 100644 index 7515c68..0000000 --- a/doc/jcgp/backend/modules/problem/SymbolicRegressionProblem.html +++ /dev/null @@ -1,436 +0,0 @@ - - - - - -SymbolicRegressionProblem - - - - - - - - -
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jcgp.backend.modules.problem
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Class SymbolicRegressionProblem

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    public class SymbolicRegressionProblem
-extends TestCaseProblem<java.lang.Double>
    -
    Symbolic regression functions -

    - Using this problem type, regression problems can be solved. - parseData() must be used to load the desired function - data in the standard CGP .dat format. -

    - This problem uses quite a few parameters: -
      -
    • Error threshold: the maximum difference allowed between an - evolved output and the equivalent output from the problem data. - Outputs within the error threshold will be considered correct. - This is only used if hits is enabled.
      • -
    • Perfection threshold: if the fitness is calculated without - using the hits method, it is a decimal value. A solution is - considered perfect when the difference between its fitness and - the maximum possible fitness is within the perfection threshold.
      • -
    • Hits-based fitness: increment the fitness by 1 whenever the - chromosome output is within the error threshold.
    -
    -
    Author:
    -
    Eduardo Pedroni
    -
    See Also:
    -
    SymbolicRegressionFunctions
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      Constructor Detail

      - - - -
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        SymbolicRegressionProblem

        -
        public SymbolicRegressionProblem(Resources resources)
        -
        Creates a new instance of SymbolicRegressionProblem.
        -
        -
        Parameters:
        -
        resources - a reference to the experiment's resources.
        -
        -
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      Method Detail

      - - - -
        -
      • -

        evaluate

        -
        public void evaluate(Population population)
        -
        Description copied from class: Problem
        -
        The most important method of the problem type. This is called once - per generation, when the new population has been generated. -

        - The basic functionality of this method is to loop through all chromosomes - in the population and decode them according to the problem type. The - fitness of each chromosome is then calculated using the problem data - or otherwise (subjective problem types such as art generation might - leave fitness evaluations up to the user) and assigned to the appropriate - chromosome. -

        - In addition, realisations of this method should update the value of - bestFitness as appropriate, since the value of this parameter is displayed - if a GUI is in use.
        -
        -
        Specified by:
        -
        evaluate in class Problem
        -
        Parameters:
        -
        population - the population to be evaluated.
        -
        -
        • -
      - - - -
        -
      • -

        parseTestCase

        -
        public TestCaseProblem.TestCase<java.lang.Double> parseTestCase(java.lang.String[] inputs,
-                                                                java.lang.String[] outputs)
        -
        Description copied from class: TestCaseProblem
        -
        This method is used internally by addTestCase() in order - to appropriately parse strings into the right data type for the - test cases. Since the data type is problem-dependent, subclasses must - implement this method. This method must return a built TestCase - object from the arguments given.
        -
        -
        Parameters:
        -
        inputs - the inputs represented as strings.
        -
        outputs - the outputs represented as strings.
        -
        Returns:
        -
        the parsed test case.
        -
        -
        • -
      - - - -
        -
      • -

        hasPerfectSolution

        -
        public int hasPerfectSolution(Population population)
        -
        Description copied from class: Problem
        -
        Used to assert whether a given population contains a perfect solution - to the problem. It is up to the problem to define what qualifies - a perfect solution, as some problems (subject ones such as music and - art evolution, for example) might not have perfect solutions at all.
        -
        -
        Specified by:
        -
        hasPerfectSolution in class Problem
        -
        Parameters:
        -
        population - the population to search through for a perfect chromosome.
        -
        Returns:
        -
        the perfect solution index, if one exits, -1 if no perfect solution was found.
        -
        -
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- - - diff --git a/doc/jcgp/backend/modules/problem/TestCaseProblem.TestCase.html b/doc/jcgp/backend/modules/problem/TestCaseProblem.TestCase.html deleted file mode 100644 index 06a8841..0000000 --- a/doc/jcgp/backend/modules/problem/TestCaseProblem.TestCase.html +++ /dev/null @@ -1,320 +0,0 @@ - - - - - -TestCaseProblem.TestCase - - - - - - - - -
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  • Summary: 
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jcgp.backend.modules.problem
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Class TestCaseProblem.TestCase<U>

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    Type Parameters:
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    U - the data type of the test case.
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    Enclosing class:
    -
    TestCaseProblem<T>
    -
    -
    -
    -
    public static class TestCaseProblem.TestCase<U>
-extends java.lang.Object
    -
    Basic data type for encapsulating test cases, it simply - contains arrays of inputs and outputs and associated getters.
    -
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    Author:
    -
    Eduardo Pedroni
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      Constructor Summary

      - - - - - - - - -
      Constructors 
      Constructor and Description
      TestCase(U[] inputs, - U[] outputs) -
      Creates a new test case, inputs and outputs - must be specified upon instantiation.
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      Method Summary

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      All Methods Instance Methods Concrete Methods 
      Modifier and TypeMethod and Description
      U[]getInputs() 
      U[]getOutputs() 
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        Methods inherited from class java.lang.Object

        -equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
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      Constructor Detail

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        TestCase

        -
        public TestCase(U[] inputs,
-                U[] outputs)
        -
        Creates a new test case, inputs and outputs - must be specified upon instantiation.
        -
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        Parameters:
        -
        inputs - the array of inputs.
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        outputs - the array of outputs.
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      Method Detail

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        getInputs

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        -
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        getOutputs

        -
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        -
        the complete array of outputs.
        -
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jcgp.backend.modules.problem
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Class TestCaseProblem<T>

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    Type Parameters:
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    T - the data type to be used by the TestCaseProblem.
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    Direct Known Subclasses:
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    DigitalCircuitProblem, SymbolicRegressionProblem
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    public abstract class TestCaseProblem<T>
-extends Problem
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    Abstract model for a problem that uses test cases. A test case - problem is any problem that compares the chromosome output to - an expected output taken from a table of input-output mappings. -

    - This class defines a basic data type for storing test cases, - TestCase, and provides core functionality to add and manipulate - test cases in the problem. A subclass of TestCaseProblem - must simply override parseTestCase() to convert parsed - problem data strings into the required data type (T).
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    Author:
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    Eduardo Pedroni
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    See Also:
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    Problem
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      Method Detail

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        getTestCases

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        public javafx.collections.ObservableList<TestCaseProblem.TestCase<T>> getTestCases()
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        a list containing the test cases.
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        addTestCase

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        public final void addTestCase(java.lang.String[] inputs,
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        Adds test cases to the problem instance as they get parsed from a - problem data file. This template method uses parseTestCase, which - must be implemented by subclasses.
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        Parameters:
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        inputs - the inputs represented as strings.
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        outputs - the outputs represented as strings.
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        clearTestCases

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        public void clearTestCases()
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        Remove all test cases.
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        parseProblemData

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        public void parseProblemData(java.io.File file,
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        Description copied from class: Problem
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        Parses the specified file and uses the parsed data to - set up the problem type instance appropriately. Any necessary - resource changes can be performed using the provided ModifiableResources - instance. -

        - In addition, realisations of this method should update the value of - maxFitness where appropriate, as this may be displayed to the user - if a GUI is in use.
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        Specified by:
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        parseProblemData in class Problem
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        file - the data file to parse.
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        resources - a modifiable reference to the experiment's resources.
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        hasImprovement

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        public int hasImprovement(Population population)
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        Description copied from class: Problem
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        Used to assert whether a given population has a chromosome that is an improvement over - the current best chromosome. A typical implementation of this method - will simply compare chromosome fitness values, though the problem type - is free to implement this in any way.
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        Specified by:
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        hasImprovement in class Problem
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        Parameters:
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        population - the population potentially containing a fitter chromosome.
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        Returns:
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        the index of the first chromosome in the population that is an improvement, -1 if none is found.
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- - - diff --git a/doc/jcgp/backend/modules/problem/TravellingSalesmanProblem.html b/doc/jcgp/backend/modules/problem/TravellingSalesmanProblem.html deleted file mode 100644 index 4a2d0c9..0000000 --- a/doc/jcgp/backend/modules/problem/TravellingSalesmanProblem.html +++ /dev/null @@ -1,433 +0,0 @@ - - - - - -TravellingSalesmanProblem - - - - - - - - -
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jcgp.backend.modules.problem
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Class TravellingSalesmanProblem

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    public class TravellingSalesmanProblem
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    Travelling salesman problem -

    - Using this problem type, travelling salesman tours can be evolved. - parseData() must be used to load the desired city - coordinates in the standard .tsp format.
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    Author:
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    Eduardo Pedroni
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    TravellingSalesmanFunctions
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      Constructor Detail

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        TravellingSalesmanProblem

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        public TravellingSalesmanProblem(Resources resources)
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        Construct a new instance of TravellingSalesmanProblem.
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        evaluate

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        public void evaluate(Population population,
-                     Resources resources)
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        Description copied from class: Problem
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        The most important method of the problem type. This is called once - per generation, when the new population has been generated. -

        - The basic functionality of this method is to loop through all chromosomes - in the population and decode them according to the problem type. The - fitness of each chromosome is then calculated using the problem data - or otherwise (subjective problem types such as art generation might - leave fitness evaluations up to the user) and assigned to the appropriate - chromosome. -

        - In addition, realisations of this method should update the value of - bestFitness as appropriate, since the value of this parameter is displayed - if a GUI is in use.
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        Specified by:
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        evaluate in class Problem
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        Parameters:
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        population - the population to be evaluated.
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        resources - parameters and utilities for optional reference.
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        isPerfectSolution

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        public boolean isPerfectSolution(Chromosome fittest)
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        Description copied from class: Problem
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        Used to assert whether a given chromosome is a perfect solution - to the problem. It is up to the problem to define what qualifies - a perfect solution, as some problems (subject ones such as music and - art evolution, for example) might not have perfect solutions at all. -

        - Note that if this method returns true, the experiment will move on - to the next run, or finish if no more runs are left.
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        Specified by:
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        isPerfectSolution in class Problem
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        Parameters:
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        fittest - the potentially perfect chromosome.
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        Returns:
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        true if the argument is a perfect solution.
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        parseProblemData

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        public void parseProblemData(java.io.File file,
-                             ModifiableResources resources)
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        Description copied from class: Problem
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        Parses the specified file and uses the parsed data to - set up the problem type instance appropriately. Any necessary - resource changes can be performed using the provided ModifiableResources - instance. -

        - In addition, realisations of this method should update the value of - maxFitness where appropriate, as this may be displayed to the user - if a GUI is in use.
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        Specified by:
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        parseProblemData in class Problem
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        Parameters:
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        file - the data file to parse.
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        resources - a modifiable reference to the experiment's resources.
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        isImprovement

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        public boolean isImprovement(Chromosome fittest)
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        Description copied from class: Problem
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        Used to assert whether a given chromosome is an improvement over - the current best chromosome. A typical implementation of this method - will simply compare chromosome fitness values, though the problem type - is free to implement this in any way.
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        Specified by:
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        isImprovement in class Problem
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        Parameters:
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        fittest - the potentially fitter chromosome.
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