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- Suppose a Genetic Algorithm uses chromosomes of the form x=abcdef with a fixed length of six genes.Each gene can be any digit between 0 and 9 . Let the fitness of individual x be calculated as : f(x) =(a+b)-(c+d)-(e+f) And let the initial population consist of four individuals x1, ... ,x4 with the following chromosomes : x1 = 3 5 3 2 6 5, x2 = 9 8 0 1 2 2, x3 = 1 2 2 1 2 3, x4 = 7 9 2 3 1 1 1. 1.Evaluate the fitness of each individual, showing all your workings, and arrange them in order with the fittest first and the least fit last. 2. calculate the average fitness. 3. Perform the following crossover operations:a- Cross the fittest two individuals using one point crossover at the middle point.b- Cross the second and third fittest individuals using a two point crossover (points b and e). Suppose the new population consists of the four offspring individuals received by the crossover operations in the above question. Evaluate the fitness of the new population, showing all your…Computer Science Write a code (in Python) for a simulated (or actual) bipedal robot, that will allow it to maintain its balance, while withstanding external perturbations.How would you recommend fixing a Genetic Algorithm's local solution issue so that it could find a global optimum solution?
- Suppose a genetic algorithm uses chromosomes of the form x = abcdefgh with a fixed length of eight genes. Each gene can be any digit between 0 and 9. Let the fitness of individual x be calculated as: f(x) = (a + b) – (c + d) + (e + f) – (g + h) and let the initial population consist of four individuals with the following chromosomes: x1 = 6 5 4 13 5 32 x2 = 87 126601 x3 = 2392 1 285 x4 = 4185 2094 1. Evaluate the fitness of each individual, showing all your workings, and arrange them in order with the fittest fiırst and the least fit last. 2. Perform the following crossover operations: One-point crossover, Two-point crossover, and uniform crossover.Design turing machine for L = {0^ 1" 0" | n2 1}.Consider the following procedure for initializing the parameters of a neural network: 1. Pick a random number r r = rand(1,1) * (2 + INIT_EPSILON ) – INIT_EPSILON 2. Set e =r for all i, j,l Does this work? No, because the procedure fails to break symmetry. O b. Yes, unless we are unlucky and get r = 0 (up to numerical precision). O. Yes or no, depending on the training set inputs x(i). d. Yes, because the parameters are chosen randomly.
- For example, in the following simple Turing Machine, the operation may never halt (infinite loop): Turing Machine M = (S, E, 8, s, {h}) where S = {q, h} E = {a, u, } 8 is given by the following transition functions: 1. ((q, a) , (q, +)) 2. ((q, u) , (h, u)) 3. (q, >), (q, →)) Note: u represents the blank symbol.Derive the way of reducing simulation error through mathematical equations.Computer science. Correct answer will be upvoted else downvoted. Think about a n by n chessboard. Its columns are numbered from 1 to n from the top to the base. Its sections are numbered from 1 to n from the passed on to one side. A cell on a convergence of x-th line and y-th section is indicated (x,y). The fundamental corner to corner of the chessboard is cells (x,x) for all 1≤x≤n. A stage of {1,2,3,… ,n} is composed on the fundamental slanting of the chessboard. There is actually one number composed on every one of the cells. The issue is to segment the cells under and on the principle askew (there are by and large 1+2+… +n such cells) into n associated areas fulfilling the accompanying imperatives: Each district ought to be associated. That implies that we can move from any cell of a locale to some other cell of a similar area visiting just cells of a similar district and moving from a cell to a neighboring cell. The x-th area ought to contain cell on the fundamental…