……….is an algorithm, a loop that continually moves in the direction of increasing value – that is uphill.
a) Up-Hill Search
b) Hill-Climbing
c) Hill algorithm
d) Reverse-Down-Hill search
Explanation: Refer the definition of Hill-Climbing approach.
When will Hill-Climbing algorithm terminate?
a) Stopping criterion met
b) Global Min/Max is achieved
c) No neighbor has higher value
d) All of the mentioned
Explanation: When no neighbor is having higher value, algorithm terminates fetching local min/max.
What are the main cons of hill-climbing search
a) Terminates at local optimum & Does not find optimum solution
b) Terminates at global optimum & Does not find optimum solution
c) Does not find optimum solution & Fail to find a solution
d) Fail to find a solution
Explanation: Algorithm terminates at local optimum values, hence fails to find optimum solution.
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
Explanation: Refer to the definition of variants of hill-climbing search.
Hill climbing sometimes called…because it grabs a good neighbor state without thinking ahead about where to go next.
a) Needy local search
b) Heuristic local search
c) Greedy local search
d) Optimal local search
Hill-Climbing approach stuck for which of the following reasons?
a) Local maxima
b) Ridges
c) Plateaux
d) All of these
Explanation: Local maxima: a local maximum is a peak that is higher than each of its neighboring states, but lower than the global maximum.
Ridges: Ridges result in a sequence of local maxima that is very difficult for greedy algorithms to navigate.
Plateaux: a plateau is an area of the state space landscape where the evaluation function is flat.
