Genetic algorithm population

 PackageChapter2;Importjava.util.Arrays;ImportJava.util.Comparator;ImportJava.util.Random;/*** A population is a abstraction of a collection of individuals. The population * class is generally used to perform group-level operations on its individuals, * such as finding the Stron Gest individuals, collecting stats on the population * as a whole, and selecting individuals to mutate or crossover. *  * @authorBkanber **/ Public classPopulation {Privateindividual population[]; Private Doublepopulationfitness =-1; /*** Initializes blank population of individuals * *@paramPopulationsize * The number of individuals in the population*/     PublicPopulation (intpopulationsize) {        //Initial Population         This. Population =NewIndividual[populationsize]; }    /*** Initializes population of individuals * *@paramPopulationsize * The number of individuals in the population *@paramChromosomelength * The size of each individual ' s chromosome*/     PublicPopulation (intPopulationsize,intchromosomelength) {        //Initialize The population as an array of individuals         This. Population =NewIndividual[populationsize]; //Create Each individual in turn         for(intIndividualcount = 0; Individualcount < populationsize; individualcount++) {            //Create An individual, initializing it chromosome to the given//lengthIndividual individual =Newindividual (chromosomelength); //Add individual to population             This. population[individualcount] =individual; }    }    /*** Get individuals from the population * *@returnindividuals individuals in population*/     Publicindividual[] Getindividuals () {return  This. Population; }    /*** Find A individual in the population by it fitness * * This method lets you select a individual in O Rder of its fitness. This * can is used to find the single strongest individual (eg, if you ' re * testing for a solution), but it can al So is used to find weak individuals * (if you ' re looking to cull the population) or some of the strongest * indivi     Duals (If you ' re using "elitism"). *      * @paramOffset * The offset of the individual want, sorted by fitness. 0 is * the strongest     , Population.length-1 is the weakest. * @returnindividual individual at offset*/     PublicIndividual Getfittest (intoffset) {        //Order population by fitnessArrays.sort ( This. Population,NewComparator<individual>() {@Override Public intCompare (individual O1, individual O2) {if(O1.getfitness () >o2.getfitness ()) {                    return-1; } Else if(O1.getfitness () <o2.getfitness ()) {                    return1; }                return0;        }        }); //Return the fittest individual        return  This. Population[offset]; }    /*** Set population ' s Group Fitness * *@paramFitness * The population ' s total Fitness*/     Public voidSetpopulationfitness (DoubleFitness) {         This. populationfitness =Fitness; }    /*** Get population ' s Group Fitness * *@returnpopulationfitness The population ' s total Fitness*/     Public Doublegetpopulationfitness () {return  This. populationfitness; }    /*** Get Population ' s size * *@returnsize The population ' s size*/     Public intsize () {return  This. Population.length; }    /*** Set Individual at offset * *@paramIndividual *@paramOffset *@returnindividual*/     PublicIndividual setindividual (intoffset, individual individual) {        returnPopulation[offset] =individual; }    /*** Get Individual at offset * *@paramOffset *@returnindividual*/     PublicIndividual getindividual (intoffset) {        returnPopulation[offset]; }        /*** Shuffles The population in-place * *@paramvoid *@returnvoid*/     Public voidShuffle () {Random rnd=NewRandom ();  for(inti = population.length-1; i > 0; i--) {            intindex = rnd.nextint (i + 1); Individual A=Population[index]; Population[index]=Population[i]; Population[i]=A; }    }}

Genetic algorithm population