{"id":17410,"date":"2024-04-15T05:13:03","date_gmt":"2024-04-15T05:13:03","guid":{"rendered":"https:\/\/exam.pscnotes.com\/mcq\/?p=17410"},"modified":"2024-04-15T05:13:03","modified_gmt":"2024-04-15T05:13:03","slug":"stochastic-hill-climbing-chooses-at-random-from-among-the-uphill-moves-the-probability-of-selection-can-vary-with-the-steepness-of-the-uphil1-move-a-true-b-false","status":"publish","type":"post","link":"https:\/\/exam.pscnotes.com\/mcq\/stochastic-hill-climbing-chooses-at-random-from-among-the-uphill-moves-the-probability-of-selection-can-vary-with-the-steepness-of-the-uphil1-move-a-true-b-false\/","title":{"rendered":"Stochastic hill climbing chooses at random from among the uphill moves; the probability of selection can vary with the steepness of the uphil1 move. A. True B. False"},"content":{"rendered":"

[amp_mcq option1=”TRUE” option2=”nan” option3=”nan” option4=”nan” correct=”option1″]<\/p>\n

The correct answer is False. Stochastic hill climbing is a local search algorithm that iteratively moves from one solution to another in the search space, always choosing the move that improves the objective function the most. However, it can sometimes get stuck in local minima, where there are no moves that improve the objective function. To avoid this, stochastic hill climbing can be combined with a random restart, where the algorithm is restarted from a random solution. This can help the algorithm to escape from local minima and find a better solution.<\/p>\n

The statement “Stochastic hill climbing chooses at random from among the uphill moves; the probability of selection can vary with the steepness of the uphill move” is false. Stochastic hill climbing always chooses the move that improves the objective function the most, regardless of the steepness of the uphill move. This is because the goal of stochastic hill climbing is to find the best possible solution, not just a good solution.<\/p>\n","protected":false},"excerpt":{"rendered":"

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