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- How experiments is to analyze scalability of different multiobjective EDAs and other evolutionary algorithms on the aforementioned multiobjective decomposable problems.The algorithm for computing the length of the longest common prefix between P[i, . . . , m] and T[j, . . . , n] and the improved algorithm for finding the Combinatorial Pattern Matching Algorithms in Computational Biologyoccurrences of sequence P[1, . . . , m] in sequence T[1, . . . , n] can both be implemente in R script.Based on the cyclomatic complexity measured in previous question, identify the basis set of independent paths.
- Please don't copy Consider the following knowledge base a.Prove that Q is true with: 1. P → Q 2. L ∧ M → P 3. B ∧ L → M 4. A ∧ P → L 5. A ∧ B → L 6. A 7. B i. Forward-Chaining ii. Backward-Chaining iii. ResolutionJustify your usage of a particular machine learning model and the problem it could solve. Two primary approaches exist for classifying objects: (A) relying on memory and (C) using a K-nearest neighbour algorithm.whose responsability is it to ensure that algorithms are not discriminatory?
- Assume we have a robot in the Vacuum-world Problem which described in our lectures. If weextend the environment of this problem to be three squares instead of two squares (described in thisfigure), generate the State Space Graph of this game and compare its complexity with its smallversion (2-squares environment)Which of the following diagrams do not contradict the current state of our knowledge about the complexity classes P, NP, and NPC (NP- complete problems)? Explain briefly.What is the output of this algorithm? Determine the iterative correlation for the time complexity. Solve this relation. Which design technique is used in this algorithm? Is this algorithm better than the brute force algorithm which gives the same result? Why is that?
- PLEASE PROVE IN ACADEMIC WAY What is the time complexity of the Dynamic Programming Algorithm in the last example of the relevant slides ? Give an asymptotically tight bound (Θ(?)). Prove your answer.Please answer all 4 questions in detailes. Number 3 is already answered it needs graph which I already provided. Thanks 1. What are the worst-, average-, and best-case asymptotic run times for each of the algorithms you implemented? Give intuitive arguments as to why these are the asymptotic run times. 2. Under what circumstances are these asymptotic run times achieved (i.e. for what kind of vectors, sorted, reversed, something else)? 3. Generate three graphs, one for best-, average-, and worst-case scenarios, using the data collected in your experiments and the plotData executable provided on Blackboard. plotData takes three arguments: (a) --data is the file holding the relevant data. (b) --case should be Best, Worst, or Average. (c) --save is the file where the resulting figure will be saved. For example, ./plotData --data best.csv --case Best --save best_fig generates figures for best-case run times in the file best.csv and saves the figure as best_fig.png. 4. For each of the graphs…correct and detailed answer will be upvoted else downvoted. For a variety of a decent numbers a, one discovered that the amount of approximately two adjoining components is equivalent to x (for example x=ai+ai+1 for some I). x had ended up being a decent number too. Then, at that point, the components of the exhibit a were worked out in a steady progression without separators into one string s. For instance, assuming a=[12,5,6,133], s=1256133. You are given a string s and a number x. Your undertaking is to decide the situations in the string that compare to the neighboring components of the cluster that have total x. In case there are a few potential replies, you can print any of them. Input :The primary line contains the string s (2≤|s|≤5⋅105). The subsequent line contains an integer x (2≤x<10200000). An extra imperative on the input: the appropriate response consistently exists, i.e you can generally choose two nearby substrings of the string s so that in the event that…