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Which of the following techniques can be called as intelligent exhaustive search?

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Final answer:

The brute-force approach can be considered an intelligent exhaustive search technique, methodically trying all possible candidates for a solution. Heuristics are mental shortcuts used for faster problem-solving, in contrast to strict algorithms. So, the correct answer is 3. Brute-force approach.

Step-by-step explanation:

Among the techniques listed, brute-force approach is considered an intelligent exhaustive search strategy.

The brute-force approach systematically enumerates all possible candidates for the solution and checks whether each candidate satisfies the problem's statement.

While not efficient for all problem types due to its nature of trying all possible solutions, it is a straightforward and surefire way to find a solution if one exists.

The other techniques mentioned, such as random sampling, greedy algorithm, and heuristic optimization, apply different strategies and shortcuts to reduce the search space and often do not guarantee finding the best solution.

A mental shortcut in the form of a general problem-solving framework is called a heuristic.

Unlike an algorithm, which is a problem-solving strategy characterized by a specific set of instructions, a heuristic provides a general guidance, allowing for faster decision-making at the cost of accuracy.

For example, random sampling is a basic sampling technique which involves randomly selecting items from a population.

Stratified sampling involves dividing the population into strata and then sampling from these strata.

Systematic sampling selects samples following a fixed periodic interval, and cluster sampling selects entire groups or clusters of subjects, such as all individuals within a selected location.

Lastly, convenience sampling involves selecting subjects that are easiest to access.

Thus, the correct answer is 3. Brute-force approach.

Question: Which of the following techniques can be called as intelligent exhaustive search?

  1. Random sampling
  2. Greedy algorithm
  3. Brute-force approach
  4. Heuristic optimization
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