Instance of Abstract class

Hello friends
I know that abstract class cannot be instanciated but then I really wonder how Calendar class gets instanciated..
Calendar class is a abstract class but it has a method getInstance().
How it might have been done in Calendar class when it is abstract ?
Can anyone help ?
Thanks and Regads
Rohit.

Yepp -:) Through the use of anonymous innerclasses
you can indirectly instantiate abstract classesand
interfaces. Basically you create a concrete classwith
no name but of the abstract class type orinterface type.
That creates an instance of the concrete sub-class,
not the abstract class.This is exactly what both I and the OP said. Is it
only valid when you say it?It's also what I said with a post that started with "Nope...". It sounded like you were disagreeing with me when you said "Yepp....".

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    bestam wrote:
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    Edited by: malcolmmc on May 14, 2009 9:20 AM

  • Abstract class 'Graphics2D'

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  • Abt Abstract Class

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  • Why cannot create instance to an abstract class?

    Dear Developers....
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  • No instances can be created from abstract classes

    Dear SAPGurus,
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  • Question about abstract classes and instances

    I have just read about abstract classes and have learned that they cannot be instantiated.
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    Roxxor wrote:
    Ok, but why is it necessary with constructors in abstract classes if we don�t use them (because what I have understand, constructors are used to create objects)?Constructors don't create objects. The new operator creates objects. An object's c'tor is invoked after the object has already been created. The c'tors job is to initialize the newly-created object to a valid state. Whenever a child object is created, the parent's c'tor is run before the child's c'tor, so that by the time we're inside the child's c'tor, setting up the child's state, we know that the parent (or rather the "parent part" of the object we're initializing) is in a valid state.
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    1.1) If you do not define an explicit constructor for your class, the compiler provides a implicit constructor that takes no args and simply calls super().
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    1.3) Constructors are not inherited.
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  • Instance of one of implementations of abstract class depending on context??

    Hi all,
    I just wonder if it is possible in Java to call creation of a new instance of an implementation of an abstract class depending on context.
    I mean something like:
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    class Concrete1 extends Abstract
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    class Concrete2 extends Abstract
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    Some more concrete code if it helps:
    Abstract class:
    * Individual.java
    * Created on 21. leden 2007, 1:08
    package genetics;
    * Abstract class defining fields and methods of one individual for genetic algorithms
    * This class is supposed to be implemented according to problem to be solved
    * @author Richard V�tek
    * @version 1.0
    abstract public class Individual implements Comparable<Individual>
       * Create random chromosomes for this individual
      protected abstract void createRandomChromosomes();
       * Count fitness of this individual
       * This number says how good is this individual (the higher number the better).
       * Better fitness means that this individual is closer to solution.
       * @return  int   Fitness of this individual
      protected abstract int getFitness();
       * Cross-breed this individual with another individual
       * This leaves untouched number of chromosomes to certain (randomly generated) position.
       * From this position on, it will swap chromosomes between this and another individual.
       * So this individual gets changed (cross-breeded) as well as the other, which is returned
       * as result of this method.
       * @param   other              The other individual to cross-breed with
       * @return  Individual         Hybrid of this and another individual (in fact, the other individual
       *                             after cross-breed (this (source) individual gets changed too after cross-breed)
      protected abstract Individual crossbreed(Individual other);
       * Mutate this individual
       * Mutate chromosomes of this individual; every chromosome is mutated
       * with probability set in settings of evolution.
       * This probability is supposed to be quite low number, roughly 1 %.
      protected abstract void mutate();
       * Check this individual
       * Check if this individual still suits the assignment.
       * If not, repair this individual to suit it again.
      protected abstract void check();
       * Implementation of Comparable: comparing of individuals by fitness
       * @param other Another individual to compare
      public int compareTo(Individual other)
        return this.getFitness() - other.getFitness();
    One implementation class:
    * KnapsackIndividual.java
    * Created on 21. leden 2007, 1:41
    package genetics;
    import java.util.Random;
    import java.util.TreeMap;
    import knapsack.KnapsackProblem;
    * This is practically the same as KnapsackProblem class but designed specially
    * for solving knapsack problem with genetic algorithm so all unnecessary fields
    * and methods are removed.
    * @author Richard V�tek
    * @version 1.0
    public class KnapsackIndividual extends Individual
       * Chromosomes of this individual
       * In case of knapsack problem, they are things currentl in knasack
      protected boolean[] arrChromosomes;
       * Cached value of fitness of this individual
       * Used to not to count fitness of this individual everytime when needed
       * (and it is often needed); once counted, it will be read from this cached value
      private int intFitnessCache = Integer.MIN_VALUE;
       * Randomizer for random-driven methods (like mutation, etc.)
      private Random randomizer = new Random();
       * Reference to evolution to read mutation probability from
      protected Evolution evolution;
       * Assignment of problem instance
      protected KnapsackProblem assignment;
       * Create a new Individual instance
       * @param   assignment  Object representing assignment of particular problem
       * @param   evolution   Reference to evolution object to be able to read evolution's settings from
      public KnapsackIndividual(KnapsackProblem assignment, Evolution evolution)
        this.assignment = assignment;
        this.evolution = evolution;
        this.arrChromosomes = new boolean[assignment.getNumberOfThings()];
       * Create random chromosomes for this individual
       * @see Individual#createRandomChromosomes()
      protected void createRandomChromosomes()
        int intChromosomeCount = this.arrChromosomes.length;
        for (int i = 0; i < intChromosomeCount; i++)
          this.arrChromosomes[i] = this.randomizer.nextBoolean();
       * Get fitness of this individual
       * In case of knapsack, fitness is sum of prices of all things currently in knapsack
       * @see Individual#getFitness()
      protected int getFitness()
        //if cached value exist, return it
        if (this.intFitnessCache != Integer.MIN_VALUE)
          return this.intFitnessCache;
        //otherwise, count fitness of this individual
        int intChromosomeCount = this.arrChromosomes.length;
        int intSumOfValues = 0;
        //in case of knapsack, fitness is value of all things currently in knapsack
        //(sum of values of all things in knapsack)
        for (int i = 0; i < intChromosomeCount; i++)
          intSumOfValues = this.assignment.arrPrices;
    //save counted fitness to cache
    this.intFitnessCache = intSumOfValues;
    return intSumOfValues;
    * Cross-breed two individuals
    * @param other The other individual for cross-breed
    * @return The other individual after cross-breed (but this individual is affected too)
    * @see Individual#crossbreed()
    protected Individual crossbreed(Individual other)
    int intChromosomeCount = this.arrChromosomes.length;
    //position from which on swap chromosomes of this and the other individual
    int intCrossbreedPosition = this.randomizer.nextInt(intChromosomeCount);
    boolean booTemp;
    //swap chromosomes from cross-breed position on
    for (int i = intCrossbreedPosition; i < intChromosomeCount; i++)
    booTemp = ((KnapsackIndividual) this).arrChromosomes[i];
    ((KnapsackIndividual) this).arrChromosomes[i] = ((KnapsackIndividual) other).arrChromosomes[i];
    ((KnapsackIndividual) other).arrChromosomes[i] = booTemp;
    return other;
    * Mutate individual chromosomes of this individual with certain probability
    * In case of knapsack, particular thing is just inserted/taken out of the knapsack
    * @see Individual#mutate()
    protected void mutate()
    //probability of mutation (in percents)
    int intMutationProbability = this.evolution.getMutationProbability();
    int intChromosomeCount = this.arrChromosomes.length;
    //iterate through all chromosomes, mutating them with certain (set) probability
    for (int i = 0; i < intChromosomeCount; i++)
    //mutation probability passed => mutate this chromosome
    if (this.randomizer.nextInt(100) < intMutationProbability)
    this.arrChromosomes[i] = !this.arrChromosomes[i];
    //when mutation finished, we must check if this individual still suits the assignment;
    //if not, repait it
    this.check();
    * Check if this individual still suits the assignment; if not, repair it
    * In case of knapsack it means that sum of weights of things currently in knapsack
    * will not exceed capacity of backpack; if exceeds, take out as many things as necessary
    * to not to exceed again; choose things to be taken out according to worst weight to price ratio
    * @see Individual#check()
    protected void check()
    int intSumOfWeights = 0;
    //list of things in the knapsack sorted by weight to price ratio
    //key: index of thing in knapsack
    //value: weight/price ratio
    TreeMap<Integer, Float> things = new TreeMap<Integer, Float>();
    for (int i = 0; i < this.arrChromosomes.length; i++)
    //thing in the knapsack
    if (this.arrChromosomes[i] == true)
    //add its weight to sum of weights
    intSumOfWeights += this.assignment.arrWeights[i];
    //add it to the list of things sorted by weight to price ratio
    things.put(i, (((float) this.assignment.arrWeights[i]) / ((float) this.assignment.arrPrices[i])));
    //sum of weights exceeds knapsack capacity => take out as many things as necessary
    while (intSumOfWeights > this.assignment.getKnapsackCapacity())
    //take out thing with worst weight/price ratio from all things currently in knapsack
    this.arrChromosomes[things.lastKey()] = false;
    //update sum of weights of things currently in knapsack
    intSumOfWeights -= things.get(things.lastKey());
    //also remove this thing from list of things
    things.remove(things.lastKey());
    And another class, where i need this feature (tried to use generics for that, but they can't be used in this way):
    * Evolution.java
    * Created on 21. leden 2007, 2:47
    package genetics;
    import java.io.BufferedReader;
    import java.io.FileReader;
    import java.io.IOException;
    import java.util.Arrays;
    * Class for algorithms using simulated evolution to deal with a problem
    * Tried to be designed general enough to allow to be used for every genetic algotihm.
    * If true, only class Individual must be implemented according to problem to be solved,
    * the rest of genetic algorithm stays the same.
    * @author Richard V�tek
    * @version
    public class Evolution<Problem, IndividualClass extends Individual>
       * Number of generations of evolution to finish
      protected int intGenerationCount;
       * Number of individuals in each generation
      protected int intGenerationSize;
       * Elite individual count
       * All elite individuals are just copied from one generation to another with no change
      protected int intGenerationEliteCount;
       * Number of individuals participating a tournament
       * To select an good individual for next generation, tournaments are hold.
       * This affects number of individuals which one good individual is selected
       * from in one tournament.
       * @see <a href="http://cs.felk.cvut.cz/%7Exobitko/ga/example_f.html">Genetic Algorithm Example Applet</a>
      protected int intGenerationTournamentSize;
       * Probability of mutation (in percents)
      protected int intMutationProbability;
       * Current generation of individuals in evolution
      protected Generation<IndividualClass> thisGeneration;
       * Next generation of individuals in evolution
      protected Generation<IndividualClass> nextGeneration;
       * Fitness of best individual in this generation
      private int intIndividualBestFitness;
       * Sum of fitnesses of all individuals in this generation
      private int intIndividualsSumFitness;
       * Fitness of worst individual in this generation
      private int intIndividualWorstFitness;
       * Fitness of best elite individual in (every) generation
       * Auxilliary variable to not to count statistics for elite individuals
       * in each generation as well (not needed - elite individuals don't change themselves)
      private int intIndividualEliteBestFitness;
       * Sum of fitnesses of all elite individuals in (every) generation
       * Auxilliary variable to not to count statistics for elite individuals
       * in each generation as well (not needed - elite individuals don't change themselves)
      private int intIndividualElitesSumFitness;
       * Fitness of worst elite individual in (every) generation
       * Auxilliary variable to not to count statistics for elite individuals
       * in each generation as well (not needed - elite individuals don't change themselves)
      private int intIndividualEliteWorstFitness;
       * Create a new instance of Evolution (settings passed through parameters)
       * @param   intGenerationCount            Number of generation of evolution to finish
       * @param   intGenerationSize             Number of individuals in each generation
       * @param   intGenerationEliteRatio       Elite individuals to generation size ratio (in percents)
       * @param   intGenerationTournamentRatio  Members of tournament to generation size ratio (in percents)
       * @param   intMutationProbability        Probability of mutation of each chromosome of each individual of generation (in percents)
       * @see     #intGenerationEliteCount
       * @see     #intGenerationTournamentSize
      public Evolution(
        int intGenerationCount, int intGenerationSize,
        int intGenerationEliteRatio, int intGenerationTournamentRatio,
        int intMutationProbability)
        this.intGenerationCount = intGenerationCount;
        this.intGenerationSize = intGenerationSize;
        this.intGenerationEliteCount = (int) (intGenerationSize * (intGenerationEliteRatio / 100.0));
        this.intGenerationTournamentSize = (int) (intGenerationSize * (intGenerationTournamentRatio / 100.0));
        this.intMutationProbability = intMutationProbability;
       * Create a new instance of Evolution (settings loaded from settings file)
       * @param   strSettingFile  Name of file containing settings for evolution
       * @throws  IOException     File does not exist, cannot be read, etc.
       * @throws  Exception       Another exception occured during loading of file
      public Evolution(String strSettingFile)
        BufferedReader settings;
        String settingsLine;
        int intLineCounter = 0;
        int intSetting;
        try
          settings = new BufferedReader(new FileReader(strSettingFile));
          while ((settingsLine = settings.readLine()) != null)
            intLineCounter++;
            settingsLine = settingsLine.substring(0, settingsLine.indexOf("\t"));
            intSetting = Integer.parseInt(settingsLine);
            switch (intLineCounter)
              case 1:
                this.intGenerationCount = intSetting;
                break;
              case 2:
                this.intGenerationSize = intSetting;
                break;
              case 3:
                this.intGenerationEliteCount = (int) (this.intGenerationSize * (intSetting / 100.0));
                break;
              case 4:
                this.intGenerationTournamentSize = (int) (this.intGenerationSize * (intSetting / 100.0));
                break;
              case 5:
                this.intMutationProbability = intSetting;
                break;
            } //switch
          } //while
          //after reading has been completed, let's close the stream
          settings.close();
        } //try
        //IO exception - file not found, cannot be read, etc.
        catch (IOException ioe)
          System.out.println("Vyskytl se I/O probl�m p&#345;i na&#269;�t�n� zad�n� ze souboru " + strSettingFile);
        //Exception - another problem during reading of file
        catch (Exception e)
          System.out.printf("Vyskytl se n&#283;jak� divn� probl�m p&#345;i na&#269;�t�n� zad�n� ze souboru %s. V�pis vyj�mky:\n", strSettingFile);
          e.printStackTrace();
       * Vivify first generation for evolution
       * Necessary number of individuals is created with random chromosomes.
       * Their chromosomes must then be checked if they suit the assignment
       * and if not so, repaired.
      private Generation<IndividualClass> vivifyFirstGeneration()
        //create a brand-new generation
        Generation generation = new Generation<IndividualClass>(this);
        int intTemp;
        //for all individual of this generation
        for (int i = 0; i < this.intGenerationSize; i++)
    //create an individual with no chromosomes
    generation.arrIndividuals[i] = new IndividualClass(this, Problem);
          //create a set of random chromosomes
          neration.arrIndividuals.createRandomChromosomes();
    //if these chromosomes does not suit assignment, repair them
    generation.arrIndividuals[i].check();
    //sort Individuals by fitness so elite individuals get to first positions of array
    Arrays.sort(generation.arrIndividuals);
    //now count statistics for elite individuals (it is enough to count them once,
    //elite don't get changed so their statistics don't get changed either)
    this.intIndividualEliteBestFitness = Integer.MIN_VALUE;
    this.intIndividualElitesSumFitness = 0;
    this.intIndividualEliteWorstFitness = Integer.MAX_VALUE;
    //count statistics for elite individuals
    for (int i = 0; i < this.intGenerationEliteCount; i++)
    intTemp = generation.arrIndividuals[i].getFitness();
    //better fitness than best fitness so far
    if (intTemp > this.intIndividualEliteBestFitness)
    this.intIndividualEliteBestFitness = intTemp;
    //worse fitness than worst fitness so far
    else if (intTemp < this.intIndividualEliteWorstFitness)
    this.intIndividualEliteWorstFitness = intTemp;
    this.intIndividualElitesSumFitness += intTemp;
    //reset generation's statistics
    this.intIndividualBestFitness = this.intIndividualEliteBestFitness;
    this.intIndividualsSumFitness = this.intIndividualElitesSumFitness;
    this.intIndividualWorstFitness = this.intIndividualEliteWorstFitness;
    //count generation's statistics also from rest of individuals
    for (int i = this.intGenerationEliteCount; i < this.intGenerationSize; i++)
    updateGenerationStatistics(generation.arrIndividuals[i].getFitness());
    return generation;
    * Get next generation in evolution
    * Core method for all evolution; Through this method, new generation is obtained.
    * Every next generation should contain better individuals than the previous one
    * (till certain point) so with growing number of iterations in evolution, we
    * get better results (till certain point).
    * Everytime all elite individuals are copied to next generation, then hold needed number of
    * tournaments to choose a pair of good-looking individuals, cross-breed individuals in these
    * pairs, mutate them (and repair if necessary) and finally add to next generation.
    * @return Generation Next generation in evolution
    * @see Generation#tournament()
    * @see Generation#crossbreed()
    * @see Generation#mutate()
    private Generation getNextGeneration()
    Generation nextGeneration = new Generation(this);
    //number of pairs of individuals to select for next generation
    int intIndividualPairsToSelect = (this.intGenerationSize - this.intGenerationEliteCount) / 2;
    int intTemp;
    //reset generation's statistics
    this.intIndividualBestFitness = this.intIndividualEliteBestFitness;
    this.intIndividualsSumFitness = this.intIndividualElitesSumFitness;
    this.intIndividualWorstFitness = this.intIndividualEliteWorstFitness;
    //just copy all elite individuals from this generation to another
    //(they are on first positions of array of individuals)
    for (int i = 0; i < this.intGenerationEliteCount; i++)
    nextGeneration.arrIndividuals[i] = this.thisGeneration.arrIndividuals[i];
    //hold as many tournaments as necessary to select remaining number of pairs
    //of good-looking individuals for next generation (apart from the elite ones)
    for (int i = 0; i < intIndividualPairsToSelect; i++)
    this.thisGeneration.tournament();
    this.thisGeneration.crossbreed();
    this.thisGeneration.mutate();
    //add this individual in next generation
    nextGeneration.arrIndividuals[2 * i] = this.thisGeneration.nextGenerationIndividual01;
    //update statistics of generation
    updateGenerationStatistics(this.thisGeneration.nextGenerationIndividual01.getFitness());
    //add this individual in next generation
    nextGeneration.arrIndividuals[2 * i + 1] = this.thisGeneration.nextGenerationIndividual02;
    //update statistics of generation
    updateGenerationStatistics(this.thisGeneration.nextGenerationIndividual02.getFitness());
    //next generation is complete => return it
    return nextGeneration;
    * Update statistics of current generations
    * @param intFitness Fitness that may possibly update generation's statistics
    * (best fitness, worst fitness, sum of fitnesses)
    private void updateGenerationStatistics(int intFitness)
    //better fitness than best fitness so far
    if (intFitness > this.intIndividualBestFitness)
    this.intIndividualBestFitness = intFitness;
    //worse fitness than worst fitness so far
    else if (intFitness < this.intIndividualWorstFitness)
    this.intIndividualWorstFitness = intFitness;
    //update sum of fitnesses as well (for average fitness)
    this.intIndividualsSumFitness += intFitness;
    * Execute evolution process
    * Vivify first generation and then as many next generations as set in settings of evolution
    public void evolution()
    this.thisGeneration = vivifyFirstGeneration();
    //output generation's statistics
    System.out.printf("Generace 0:\t%d\t%d\t%d", this.getIndividualBestFitness(), this.getIndividualAverageFitness(), this.getIndividualWorstFitness());
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  • What is the diff b/w Abstract class and an interface ?

    Hey
    I am always confused as with this issue : diff b/w Abstract class and an interface ?
    Which is more powerful in what situation.
    Regards
    Vinay

    Hi, Don't worry I am teach you
    Abstract class and Interface
    An abstract class can have instance methods that implement a default behavior. An Interface can only declare constants and instance methods, but cannot implement default behavior and all methods are implicitly abstract. An interface has all public members and no implementation. An abstract class is a class which may have the usual flavors of class members (private, protected, etc.), but has some abstract methods.
    Edited by SASIKUMARA
    SIT INNOVATIONS- Chennai
    Message was edited by:
    sasikumara
    Message was edited by:
    sasikumara

  • Creating an instance of a class from within another class

    I am trying to create an instance of MCQ in the T class but I am getting this error:
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    here are the two classes:
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    please help I think I have tried everything!

    Use code tags (http://forum.java.sun.com/help.jspa?sec=formatting)
    Don't import java.lang.* . You get those automatically.
    Your MCQ class is really Q.MCQ (the way you posted, anyway).
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  • EJB question: How to use abstract class in writing a session bean?

    I had written an abstract class which implements the session bean as follow:
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    However, some several questions for me are:
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    2. If I wrote the interface LoggingSession extending EJBObject where it defined the abstract methods "void update(Object obj);" and "void setSessionContext(SessionContext ctx);", that this meant I needed to write the ScheduleSession to implement the Logging Session?
    3. I used OC4J 9.0.4. How can I define the ejb-jar.xml in this case?

    Hi Maggie,
    1. do I still need to write
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    2. this
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    Alternatively, the "ScheduleSessionBean" can extend the "Logging" class and implement the "SessionBean" interface.
    Good Luck,
    Avi.

  • ".. is an abstract class.  It can't be instantiated"

    Does anyone have an idea of how I can get rid of the above error message? Here is a bit of my code:
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    It is not possible to create instances of an abstract class.
    I hope this example helps,
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    class Automobile extends Vehicle

  • A Singleton variable in an Abstract Class

    Hello there people
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    I think the OP would want a constructor in the abstract class to update that variable. Otherwise, he'd have to remember to update it in the constructor for all concrete classes. But, you don't declare constructors for the concrete class in the abstract class. You only declare constructors for the abstract class in the abstract class.

  • Abstract method called in an abstract class

    Hello,
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    Thanks,
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    Ok, let's it is not impossible, juste that I had no idea of how doing it :-)
    The difficulty is that Algorithm will never have to deal with Chromozome itself, but always with subclass of Chromozome. This is usually not an issue, but in that case, Algorithm is required to create instances of the desired subclass of Chromozome, but without knowing in advance wich subclass will be used (I hope what I say makes any sense).
    Actually I may have found a way in the meantime, but maybe not the best one.
    I created in Algorithm an abstract method :
    protected abstract Chromozome createChromozome()The method initializePopulation will call createChromozome instead of calling directly the constructor and the initialize() method of Chromozome.
    Then subclass of Algorithm will implement the method createChromozome using the desired subclass of Chromozome.

  • Abstract Class Polymorphism?

    I want to be able to add any of my sub classes to a collection (hash set) , without duplicating the code. The superclass for all these subclasses is an abstract class.
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    At the moment I have methods for each individual class; before making the parent class abstract, I could use the same method for all, but was advised as I did not want an instance of that parent class, the abstract class was best.
    Hope this is not too vague or abstract... :)

    I think if you use the wildcard symbol it'll allow you to add a covariant to the collection, for example
    public static void addSub(List<? super NameofSuperClass> varName)
    When you can to retrieve the data, if you want any methods not in the super class you would still be to cast it.
    Is this what you meant? - Peter sorry if it wasn't helpful

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