//package geneticAlgo;import java.util.Comparator;import java.util.Arrays;import java.lang.Math;import java.
util.Random;import java.util.
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ArrayList;import java.util.Collections;public class Main { // returns decimal representation of binary matrix. Note: binary matrix starts with lowest power of 2 public static int bin_to_int(int bin_rep) { int int_rep = 0; for (int i=0; i pow(2, i); } return int_rep; } // mutates each element of the binary representation with probability p public static int mutate(int bin_rep, double p) { int cutoff = (int) (1e6 * p); Random rand = new Random(); for (int i = 0; i < bin_rep.length; i++) { // mutate if random generator below cutoff if (rand.nextInt((int) 1e6) < cutoff) { bin_repi = 1-bin_repi; } } return bin_rep; } // perform single point crossover on 2 parents. public static int crossover(int parent1, int parent2) { int child1 = new intparent1. length; int child2 = new intparent2.length; // determine single crossover point after which we swap elements in parents for children Random rand = new Random(); int crossPoint = rand.nextInt(parent1.length); // old values System. arraycopy(parent1, 0, child1, 0, crossPoint); System.arraycopy(parent2, 0, child2, 0, crossPoint); // crossover values System.arraycopy(parent2, crossPoint, child1, crossPoint, parent2.length-crossPoint); System.arraycopy(parent1, crossPoint, child2, crossPoint, parent1.length-crossPoint); int children = new int {child1, child2}; return children; } // return fitness of solution for problem 1 public static int fitness(int bin_rep) { return (int) Math.pow(bin_to_int(bin_rep), 2); } // return fitness of solution for problem 2 public static int fitness2(int bin_rep){ return (int) ((-1) * Math. pow(bin_to_int(bin_rep) – 9, 2)); } public static double avg(int children) { // return average of members of population int tot = 0; for (int child : children) { tot += bin_to_int(child); } return ((float) tot)/children.length; } // return average fitness of population public static double avg_fitness(int children) { int tot_fitness = 0; for (int child : children) { tot_fitness += fitness2(child); } return ((float) tot_fitness)/children.length; } // print out population members, binary representation, and fitness public static void print_population(int population) { for (int member : population) { System.out.println(“Member: ” + bin_to_int(member) + ” Representation: ” + Arrays.toString(member) + ” fitness: ” + fitness(member)); } } // print out populatio members, binary representation, and fitness for problem 2 public static void print_population2(int population) { for (int member : population) { System. out.println(“Member: ” + bin_to_int(member) + ” Representation: ” + Arrays.toString(member) + ” fitness: ” + fitness2(member)); } } // Genetic algorithm for first problem public static int problem1() { //ArrayList length; k++) { parentk = rand.nextInt(2); } parentsj = parent; } System.out.println(“Parents”); print_population(parents); double average = avg(parents); System.out.println(“Average: ” + average); // sort population by fitness and take 5 fittest individuals Arrays.sort(parents, (a, b) -> {return (fitness(b)-fitness(a));}); int new_parents = Arrays.copyOfRange(parents, 0, 5); int generation = 1; while (average < 29) { System. out.println(“New parents (generation ” + generation + “): “); print_population(new_parents); int count = 0; // generate children from crossover int children = new int205; for (int j = 0; j < new_parents.length; j++) { for (int k = j + 1; k < new_parents.length; k++) { int generated_children = crossover(new_parentsj, new_parentsk); if (generation == 1) { System.out.println("Crossover. Parent 1: ” + Arrays.toString(new_parentsj) + ” Parent 2: ” + Arrays.toString(new_parentsk) + ” -> ” + “Child 1: ” + Arrays.toString(generated_children0) + ” Child2: ” + Arrays.toString(generated_children1)); } childrencount = mutate(generated_children0, .05); System.out. println(“Mutation. Generated child: ” + Arrays.toString(generated_children0) + ” -> Actual child: ” + Arrays.toString(childrencount)); count++; childrencount = mutate(generated_children1, . 05); System.out.println(“Mutation. Generated child: ” + Arrays.toString(generated_children1) + ” -> Actual child: ” + Arrays.toString(childrencount)); count++; } } System.out.println(“Children: “); print_population(children); average = avg(children); System.out. println(“Average: ” + average); Arrays.sort(children, (a, b) -> { return fitness(b) – fitness(a); }); new_parents = Arrays.copyOfRange(children, 0, 5); generation++; } int soln = new_parents0; System.out.println(“Best solution: ” + bin_to_int(soln) + “. Binary rep: ” + Arrays. toString(soln) + ” fitness: ” + fitness(soln)); return bin_to_int(soln); } public static int problem2() { //ArrayList sort(parents, (a, b) -> { return (fitness2(b) – fitness2(a)); }); int new_parents = Arrays.copyOfRange(parents, 0, 5); int generation = 1; while (average < -10) { System.out.println("New parents (generation " + generation + "): "); print_population2(new_parents); int count = 0; // generate children from crossover int children = new int205; for (int j = 0; j < new_parents. length; j++) { for (int k = j + 1; k < new_parents.length; k++) { int generated_children = crossover(new_parentsj, new_parentsk); if (generation == 1) { System.out. println(“Crossover. Parent 1: ” + Arrays.toString(new_parentsj) + ” Parent 2: ” + Arrays.toString(new_parentsk) + ” -> ” + “Child 1: ” + Arrays.toString(generated_children0) + ” Child2: ” + Arrays.toString(generated_children1)); } childrencount = mutate(generated_children0, .05); System.out. println(“Mutation. Generated child: ” + Arrays.toString(generated_children0) + ” -> Actual child: ” + Arrays.toString(childrencount)); count++; childrencount = mutate(generated_children1, .05); System.out.println(“Mutation. Generated child: ” + Arrays.toString(generated_children1) + ” -> Actual child: ” + Arrays.toString(childrencount)); count++; } } System.out.println(“Children: “); print_population2(children); average = avg_fitness(children); System.out. println(“Average fitness: ” + average); Arrays.sort(children, (a, b) -> { return fitness2(b) – fitness2(a); }); new_parents = Arrays.copyOfRange(children, 0, 5); generation++; } int soln = new_parents0; System.out. println(“Best solution: ” + bin_to_int(soln) + “. Binary rep: ” + Arrays.toString(soln) + ” fitness: ” + fitness(soln)); return bin_to_int(soln); } public static void main(String args) { int soln2 = problem2(); }}