The Optimal solution is : * a path from the initial state to a state satisfying the goal test This process of looking for the best sequence is called the solution with lowest path cost among all solutions O none
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- The best sequence is list of actions, called solution problem Path search The Optimal solution is : * a path from the initial state to a state satisfying the goal test This process of looking for the best sequence is called the solution with lowest path cost among all solutions noneA robot can move horizontally or vertically to any square in the same row or in the same column of a board. Find the number of the shortest paths by which a robot can move from one corner of a board to the diagonally opposite corner. The length of a path is measured by the number of squares it passes through, including the first and the least squares. Write the recurrence relation if you solve the problem by a dynamic programming algorithm.The search algorithm developed will be used for users to search the catalog for all items matching the search keyword(s), and there are a total of 15000 items in the catalog. During development, three different algorithms were created. • Algorithm A runs in constant time, with a maximum runtime of 1.10 seconds and returns all matching results. ● Algorithm B runs in logarithmic time, with a maximum runtime of 0.3 seconds, and returns only the first result. ● Algorithm C runs in linear time, with a maximum runtime of 1.50 seconds and returns all matching results. Which algorithm would be the least suitable for the requirements stated? In your answer, justify your choice by explaining why you picked that algorithm, and why you did not pick the other two algorithms.OND OND OND DAD
- esc A Question 14 of 20: Select the best answer for the question. 14. Use Gauss-Jordan elimination to solve the following linear system: -3x + 4y = -6 5x - y = 10 O A. (2,0) O B. (2,-5) OC. (-6,2) OD. (2,2) O Mark for review (Will be highlighted on the review page) > 2 T W S #3 E D لم G $ 4 { R C 19 F % 50 F ف G Oll O O U *00 C 8 AObject Reward Weight A 20 1 В 2 10 D 40 8 E 15 7 F 25 4 5 H. Maximum weight = 12 The above problem is a 0/1 Knapsack problem. You have to carry the different objects in your bag in a way such that the reward is maximized without exceeding the weight limit. You can carry an object exactly once but you always have to carry the object labelled “H". Assuming you are asked to use Genetic Algorithm for this problem, answer the following questions 1. Encode the problem and create an initial population of 4 different chromosomes 2. Explain what would be an appropriate fitness function for this problem. Use the fitness function and perform natural selection to choose the 2 fittest chromosomes. ' : 3. Using the selected chromosomes perform a single point crossover to get 2 offspring 4. Perform mutation and check the fitness of the final offspring. Explain your work. 2.Algorithm A search using the heuristic h(n) = α for some fixed constant α > 0 is guaranteed to find an optimal solution Select one: True False
- Voting Suppose that the votes of n people for different candidates (where there can be more than two candidates) for a particular office are the elements of a sequence. A person wins the election if this person receives a majority of the votes. 9. (a) Devise a divide-and-conquer algorithm that determines whether a candidate received a majority and, if so, determine who this candidate is. (b) Devise a divide-and-conquer algorithm that determines whether the two candidates who received the most votes each received at least n/4 votes and, if so, determine who these two candidates are. (c) Give a big-O estimate for the number of comparisons needed by the algorithm you devised in part (a).Gradient descent is a widely used optimization algorithm in machine learning and deep learning. It is used to find the minimum value of a differentiable function by iteratively adjusting the parameters of the function in the direction of the steepest decrease of the function's value. In gradient descent, the function is first differentiated to find its gradient, which is a vector of the partial derivatives with respect to each of the parameters. The gradient points in the direction of the steepest increase of the function, so to find the minimum, we need to move in the opposite direction, i.e., in the direction of the negative gradient. The algorithm starts from an initial guess for the parameters and iteratively adjusts the parameters by subtracting a small fraction of the gradient from the current parameters. The fraction is determined by the learning rate, which is a hyperparameter that controls the step size of the update. The algorithm updates the parameters…esc A Question 14 of 20: Select the best answer for the question. 14. Use Gauss-Jordan elimination to solve the following linear system: -3x + 4y = -6 5x - y = 10 O A. (2,0) O B. (2,-5) OC. (-6,2) O D. (2,2) O Mark for review (Will be highlighted on the review page) > ← 21 W = S #3 E D لم G $ 4 { R بر 19 F % 50 F ف Oll GY O U *00 C 8 A
- Calculate the optimal value of the decision parameter p in the Bresenham's circle drawing algorithm. The stepwise procedure for implementing Bresenham's algorithm for circle drawing is delineated.Remaining Time: 2 hours, 27 minutes, 02 seconds. Duestion Completion Status: Y=A'. B' + (AOB) b. Using Karnaugh map, simplify the following Boolean function F (show your grouping): [2 Marks] A B C F 1 1 1 1 1 1 1 1 1 0. 1 1 1 1 1 1 Click Save and Submit to save and submit. Click Save All Answers to save all answers. Save All A O Type here to search DLL FS F9 F10 F11 9 3 r 7 Y 8 W E R = G i JJ 立8. A school is creating class schedules for its students. The students submit their requested courses and then a program will be designed to find the optimal schedule for all students. The school has determined that finding the absolute best schedule cannot be solved in a reasonable time. Instead they have decided to use a simpler algorithm that produces a good but non-optimal schedule in a more reasonable amount of time. Which principle does this decision best demonstrate? A. Unreasonable algorithms may sometimes also be undecidable B. Heuristics can be used to solve some problems for which no reasonable algorithm exists C. Two algorithms that solve the same problem must also have the same efficiency D. Approximate solutions are often identical to optimal solutions 0000