SE 2S03 ASSIGNMENT 4Due date: 28 November, by 12:40 in class. This assignment is out of 30. With the bonus points, you can obtain 38/30. You can work individually or in a group of two. Each group member receives thesame grade. Students who want to work in groups of two should enter their names in one row inthe spreadsheethttps://docs.google.com/spreadsheets/d/1yN7PVya292D-Ilf2l-EHS3hbBInCgXCXfeMgjCLQI2M/edit?usp=sharing Groups should be finalized before November 18, and any changes after that will notbe considered. There should not be any duplicated names. Students working individually do not have to enter their names in the spreadsheet.1. IntroductionIn a genetic algorithm, a population of individuals evolves over time. The stronger individualsreproduce, and the weaker ones die. The surviving ones can also mutate. With eachindividual, there is a fitness number that indicates how strong that individual is. For moredetails, see https://en.wikipedia.org/wiki/Genetic_algorithm. A genetic algorithmterminates when a given number of iterations is reached or when a solution is found, thatis, an individual with certain fitness.The goal of this assignment is to compute an image, using a basic genetic algorithmapproach, that is close to a given image.Download and unzip http://www.cas.mcmaster.ca/~nedialk/COURSES/2s03/private/a4.zip2. Data types and functions, file a4.hUse the following file (and do not change it)#i f n d e f INCLUDED_A4_H#d e f in e INCLUDED_A4_Htypedef s tru c t { unsigned char r , g , b ; } PIXEL ;typedef s tru c t {PIXEL * data ; // pointer to an array of width * height PIXELsin t width , height ; // width and height of imagein t max_color ; // largest value of a color} PPM_IMAGE ;typedef s tru c t {PPM_IMAGE image ; // imagedouble fitness ; // fitness} Individual ;Date: 14 November, 2018.1// Read and write PPM filePPM_IMAGE * read_ppm ( const char * file_name ) ;void write_ppm ( const char * file_name , const PPM_IMAGE * image ) ;// Random image and populationPIXEL * generate_random_image ( in t width , in t height , in t max_color ) ;Individual * generate_population ( in t population_size , in t width , in theight , in t max_color ) ;// Fitnessdouble comp_distance ( const PIXEL *A , const PIXEL *B , in t image_size );void comp_fitness_population ( const PIXEL * image , Individual *individual , in t population_size ) ;// Crossovervoid crossover ( Individual * individual , in t population_size ) ;// Mutationvoid mutate ( Individual * individual , double rate ) ;void mutate_population ( Individual * individual , in t population_size ,double rate ) ;// Compute imagePPM_IMAGE * evolve_image ( const PPM_IMAGE * image , in t num_generations ,in t population_size , double rate ) ;// Free imagevoid free_image ( PPM_IMAGE * p ) ;#end i f3. Implementation (25 points)Implement the bodies of the functions that follow and store them in files with names asindicated at the heading of each subsection. Each of your files must include a4.h.3.1. Input/output, file readwriteppm.c. The input image is in a PPM P3 format, andyour program should produce an image in the same format. See https://en.wikipedia.org/wiki/Netpbm_format.PPM_IMAGE * read_ppm ( const char * file_name ) ;Reads a PPM image file and returns a pointer to a PPM_IMAGE structure containing the pixelsof the image, width, height, and max color.void write_ppm ( const char * file_name , const PPM_IMAGE * image ) ;Writes a PPM image into a file in PPM format.3.2. Generating a population, file population.c.PIXEL * generate_random_image ( in t width , in t height , in tmax_color ) ;2Returns a pointer to an array of width*height pixels. The red, green, and blue values of apixel are set randomly, where each value is >= 0 and Individual * generate_population ( in t population_size , in t width ,in t height , in t max_color ) ;Returns a pointer to an array of size population_size. Each entry is a structure of typeIndividual. For each entry in this array, the PPM_IMAGE structure should be properlyinitialized. The image should be generated using generate_random_image. The width, height and max_color should be initialized.3.3. Computing fitness, file fitness.c. Denote the input image by A and an arbitraryimage by B. Let n be the number of pixels in each of these images.Denote a pixel i in A by A(i) and in B by B(i). Letd(i) = [A(i).r ? B(i).r]2 + [A(i).g ? B(i).g]2 + [A(i).b ? B(i).b]2where r, g, b denote the values for red, green, and blue. The distance between A and B isf(A, B) =vuut代做SE 2S03作业、代写C/C++语言作业、代做algorithm作业、C/C++程序作业代写 代写R语言程序|代写Xni=1(1) d(i).double comp_distance ( const PIXEL *A , const PIXEL *B , in timage_size ) ;This function computes the distance (1) between the images at A and B, where each is of sizeimage_size.void comp_fitness_population ( const PIXEL * image , Individual *individual , in t population_size );Computes the fitness of each individual in the population. The fitness of individual[i] isthe distance between the input image and individual[i].image.data. individual is apointer to an array of size population_size.3.4. Crossover. You can use crossover.c from a4.zip.3.5. Mutation, file mutate.c.void mutate ( Individual * individual , double rate ) ;rate is the percentage of pixels to be mutated. For an image of size n = width*height,this function selects (int)(rate/100*n) pixels at random and sets random values for theRGB colors of these pixels.void mutate_population ( Individual * individual , in tpopulation_size , double rate ) ;3This function mutates all individuals starting from index population_size/4 to the end ofthe population. That is, from individual + population_size/4.3.6. Evolving an image, file evolve.c.PPM_IMAGE * evolve_image ( const PPM_IMAGE * image , in tnum_generations , in t population_size , double rate ) ;This functions takes as input a pointer to PPM_IMAGE, number of generations, populationsize, and mutation rate, and performs the following steps.(1) Generate a random population of population_size.(2) Compute the fitness of each individual.(3) Sort the individuals in non-decreasing order of fitness. The first individual will havethe closest distance to the original image, and it is the most “fitted”.(4) For generation 1 to number_generations(a) do a crossover on the population(b) mutate individuals from population_size/4 to population_size-1.(c) compute the fitness of each individual(d) sort the individuals in non-decreasing order of fitness value(5) Return a pointer to a PPM_IMAGE containing the fittest image, that is, the one withthe smallest fitness value.void free_image ( PPM_IMAGE *p ) ;Releases the memory associated with the image at lp.3.7. Main program. Use the main program from a4.zip.3.8. Makefile. Use the makefile from a4.zip.4. Performance (5 points)4.1. Profiling (1 point). Profile the execution of evolve using gprof. Discuss the computationallymost expensive parts of your code. In the printed output of gprof highlight the5 lines of code that contribute the most to the execution time. Explain why they contributethe most.4.2. Code optimization (4 points). Based on the gprof output, can you improve theperformance of your code? If so, describe how you would have done it and provide sufficientdetail showing your improvements.5. Bonus (8 points)5.1. Better algorithm (5 points). Play with various input parameters. Read about geneticalgorithms and see if you can produce better evolve, crossover, and mutate functions sothat you can obtain better images with smaller number of generations and population size.45.2. Visualizing the image evolution (3 points). You can output each image into a file.Then you can stitch them together into a video. If you obtain a good result, this is effectivefor your resume, especially if you have the animation on YouTube. To convert to jpg, orother formats, you can use the convert utility.6. ExamplesBelow on the left is an input image me.ppm and on the right is the image me2.ppm producedby make escher.Below on the left is an input image mcmaster.ppm and on the right is the image mcmaster2.ppm produced by make mcmaster.7. SubmitGroup of two: only one student should submit. SVN under directory A4:(1) The files readwriteppm.c, fitness.c, population.c, evolve.c and mutate.c.(2) The images from make escher and make mcmaster.(3) Video file, if you produce such. Hard copy containing5(1) readwriteppm.c, fitness.c, population.c, evolve.c and mutate.c.(2) The images from make escher and make mcmaster.(3) The output of valgrind on one of your executions. (Take smaller numbers forpopulation size and number of generations, as otherwise the execution will takea long time.) To obtain full marks for the implementation, valgrind must notreport any problems.(4) The gprof output and your discussion on optimization.(5) In 5.1, if you can produce better images than the two in this pdf:– the parameters that you have used– short descriptions of your functions, if they are different from the requiredin this assignment.(6) If you did 5.2, write a discussion on how the image is evolving over time, andwhat are the changes that you notice with each iteration转自：http://ass.3daixie.com/2018112991356037.html
