2012-03-21
Re: #6 Practice Midterm.
Should briefly say what hill climbing is
Chris
Chris
Should briefly say what hill climbing is<br><br>Chris
Originally Posted By: sushma
Q6) Describe each of the following local search algorithms: (a) random-hill climbing with restarts, (b) local beam search.
(a) Random Hill Climbing With Restart
Hill Climbing algorithm follows the strategy of looking at ones neighbors and always picking the neighbor which moves one in the most upward direction.
Random Hill Climbing With Restart
It starts a hill climbing algorithm on a randomly selected initial state. If it finds the solution, the algorithm stops. Otherwise, it restarts the hill climbing algorithm on the next randomly selected initial state.
(b) Local Beam Start
It keeps track of K states rather than just one state. It begins with K randomly generated states. At each step, the successor of all K states are generated. If any one is a goal, the algorithm stops. Otherwise, it selects K best successors and repeats.
Group Member: Steve Stilson and Sushma Bandekar
(a) Random Hill Climbing With Restart
Hill Climbing algorithm follows the strategy of looking at ones neighbors and always picking the neighbor which moves one in the most upward direction.
Random Hill Climbing With Restart
It starts a hill climbing algorithm on a randomly selected initial state. If it finds the solution, the algorithm stops. Otherwise, it restarts the hill climbing algorithm on the next randomly selected initial state.
(b) Local Beam Start
It keeps track of K states rather than just one state. It begins with K randomly generated states. At each step, the successor of all K states are generated. If any one is a goal, the algorithm stops. Otherwise, it selects K best successors and repeats.
Group Member: Steve Stilson and Sushma Bandekar
'''Originally Posted By: sushma'''
Q6) Describe each of the following local search algorithms: (a) random-hill climbing with restarts, (b) local beam search.<br><br>(a) Random Hill Climbing With Restart<br>Hill Climbing algorithm follows the strategy of looking at ones neighbors and always picking the neighbor which moves one in the most upward direction.<br><br>Random Hill Climbing With Restart<br>It starts a hill climbing algorithm on a randomly selected initial state. If it finds the solution, the algorithm stops. Otherwise, it restarts the hill climbing algorithm on the next randomly selected initial state.<br><br>(b) Local Beam Start<br>It keeps track of K states rather than just one state. It begins with K randomly generated states. At each step, the successor of all K states are generated. If any one is a goal, the algorithm stops. Otherwise, it selects K best successors and repeats. <br><br><br>Group Member: Steve Stilson and Sushma Bandekar
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