Virtual Labs

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Theory

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Aim

Theory

Pretest

Procedure

Simulation

Posttest

References

Contributors

Feedback

Perform and visualize Random Search

Choose difficulty:

Beginner

Intermediate

Advanced

If Random Search is used on a graph with multiple paths, what is the outcome?

a: It always finds the shortest path. Explanation

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b: It may find the goal but not necessarily the shortest path. Explanation

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c: It explores all nodes before reaching the goal. Explanation

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d: It guarantees failure in large graphs. Explanation

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Suppose Random Search is applied to find a path from A to D. If the random choices are A → B → E (dead-end), what is the algorithm likely to do next?

a: Stop immediately and report failure. Explanation

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b: Backtrack systematically like DFS. Explanation

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c: Randomly restart or choose another unexplored path. Explanation

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d: Wait until user gives another input. Explanation

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Why does Random Search not guarantee success in graph traversal?

a: Because it does not use heuristics or structured exploration. Explanation

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b: Because it explores all possible paths before stopping. Explanation

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c: Because it requires a cost function to work. Explanation

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d: Because it is slower than BFS or DFS. Explanation

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In optimization problems, Random Search is useful when:

a: The search space is small and deterministic. Explanation

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b: The search space is large, and systematic exploration is infeasible. Explanation

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c: An exact optimal solution is always needed. Explanation

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d: The solution requires heuristic knowledge. Explanation

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Which scenario best illustrates the randomness of Random Search?

a: Always producing the same path when run multiple times. Explanation

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b: Producing different paths on different runs, sometimes failing, sometimes succeeding. Explanation

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c: Always finding the shortest path regardless of graph size. Explanation

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d: Stopping only after exploring all nodes. Explanation

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