Local Search Problems and Optimization Problems MCQs Artificial Intelligence

By Prof. Fazal Rehman Shamil
Last modified on April 19th, 2021

Local Search Problems and Optimization Problems solved MCQs of Artificial Intelligence(Questions Answers).

Which of the following are the two key characteristics of the Genetic Algorithm?

(A). Crossover techniques and Fitness function

(B). Random mutation and Crossover techniques

(C). Random mutation and Individuals among the population

(D). Random mutation and Fitness function

(E). None of these

MCQ Answer: a


Searching by query on the Internet is the use of which of the following type of agent.

(A). Offline agent

(B). Online agent

(C). Both Offline and Online agent

(D). Goal-Based and Online agent

(E). None of these

MCQ Answer: d


In many problems the path to the goal is irrelevant, this class of problems can be answered using which of the following Techniques?

(A). Informed Search

(B). Uninformed Search

(C). Local Search

(D). both a and b

(E). None of these

MCQ Answer: c


A loop that constantly moves in the direction of growing value that is uphill, ……. is an algorithm.

(A). Up-Hill Search

(B). Hill-Climbing

(C). Hill algorithm

(D). Reverse-Down-Hill search

(E). None of these

MCQ Answer: b


Hill-Climbing algorithm terminates in which of the following conditions?

(A). Stopping criterion met

(B). Global Min/Max is achieved

(C). No neighbor has a higher value

(D). All of these

(E). None of these

MCQ Answer: c


Stochastic hill-climbing algorithm takes at random from the uphill moves, the probability of choice can differ with the steepness of the uphil1 move.

(A). True

(B). False

(C). Partially true

MCQ Answer: a


Hill climbing is commonly knows as ………search  because it grabs a suitable neighbor state without being thoughtful onward about where to go next.

(A). Needy local search

(B). Heuristic local search

(C). Greedy local search

(D). Optimal local search

(E). None of these

MCQ Answer: c


Which of the following algorithm keeps track of k states instead of just one.

(A). Hill-Climbing search

(B). Local Beam search

(C). Stochastic hill-climbing search

(D). Random restart hill-climbing search

(E). None of these  MCQ Answer: b


Which of the following are the main disadvantages of a hill-climbing search?

(A). Stops at local optimum and don’t find the optimum solution

(B). Stops at global optimum and don’t find the optimum solution

(C). Don’t find the optimum solution and Flop to search for a solution

(D). Fail to find a solution

(E). None of these

MCQ Answer: a


Hill-Climbing technique stuck for which of the following reasons?

(A). Local maxima

(B). Ridges

(C). Plateaux

(D). All of these

(E). None of these

MCQ Answer: d


Local search algorithms are not systematic, the main pros includes which of the followings?

(A). Less memory

(B). More time

(C). search a solution in a big infinite space

(D). Less memory and search a solution in a big infinite space

(E). None of these  MCQ Answer: d


A complete, local search algorithm forever finds target if one exists, an optimal algorithm forever finds a global minimum/maximum.

(A). True

(B). False  (C). Partially true

MCQ Answer: a


A genetic algorithm is a variant of stochastic beam search in which combining two parent states to generate Successor states

(A). True

(B). False  (C). Partially true

MCQ Answer: a

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