/* ITI 1121/1521 - Assignment 1
 * Marcel Turcotte (turcotte@site.uottawa.ca)
 */

/** 
 * A <b>Population</b> is a collection of chromosomes (each one
 * representing a candidate solution for the folding problem).  To
 * facilitate the implementation of the various methods, <b>the
 * chromosomes will always be kept in increasing value of fitness</b>.
 * <p/> 
 * <ul> 
 *  <li>The class <b>Population</b> declares a constant that
 *      specifies the default size of a population.  Its value should be
 *      1000;</li> 
 * <li>A <b>Population</b> consists of a fixed number of
 * chromosomes. The number is known when a population is created;</li>
 * <li>The class has two constructors.  The constructors are
 * responsible for creating the initial population of chromosomes.
 * One of the two constructors has two formal parameters, the size of
 * the population and the input chain. The other constructor has only
 * one parameter, the input chain. In that case, the default
 * population size is used.</li> 
 * </ul>
 */

public class Population {

    /**
     * A constructor of arity 2 to initialize the <b>Population</b>.
     * 
     * @param size is the size of the population, the number of chromosomes to be
     *             created
     * @param chain is the chain for which a fold is sought
     */

    public Population(int initialSize, String chain) {

	// REPLACE THE BODY OF THIS METHOD WITH YOUR OWN IMPLEMENTATION.

    }

    /**
     * A constructor of arity 1 to initialize the <b>Population</b>. The
     * population has the default size.
     * 
     * @param chain is the chain for which a fold is sought
     */

    public Population(String chain) {

	// REPLACE THE BODY OF THIS METHOD WITH YOUR OWN IMPLEMENTATION.

    }

    /**
     * The method <b>evolve</b> selects parent chromosomes. An offspring is then
     * created from the two parents, using the method <b>crossover</b>. The
     * offspring is <b>mutated</b>, and then inserted into the population. As a
     * result, the least fitted individual will be eliminated from the
     * population. Remember that the <b> population </b> is kept in increasing
     * order of fitness. For the selection of the parents, you can experiment
     * with different scenarios. A possible scenario is to randomly select two
     * parents. Another possible one would be to select the fittest, and a
     * randomly selected one. Or else, select the two most fitted individuals.
     */

    public void evolve() {

	// REPLACE THE BODY OF THIS METHOD WITH YOUR OWN IMPLEMENTATION.

    }

    /**
     * The instance method <b>public Chromosome getFittest()</b> returns the
     * "best" individual of the population, i.e. the one that has the smallest
     * fitness value.
     * 
     * @return returns the currently best solution
     */

    public Chromosome getFittest() {

	// REPLACE THE BODY OF THIS METHOD WITH YOUR OWN IMPLEMENTATION.

	return null;
    }

    /**
     * Returns a String representation of this Population.
     * 
     * @return the String representation of this Population
     */

    public String toString() {

	// REPLACE THE BODY OF THIS METHOD WITH YOUR OWN IMPLEMENTATION.

	return null;
    }

}