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Priyadarshini JL College
of Engineering
Topic :Hill Climbing Algorithm
Subject : Artificial Intelligence
Name :Vedant More (68)
Guide :Prof.Sonal Bawankule
CONTENT
1.Introduction
2.Characteristics
3.Algorithm
4.Example
5.Problems in Hill Climbing
6.Applications
Defination.:Basically, Hill Climbing
is variant of generate and test
method
2.In which feedback from test
procedure is used to help generator
to decide that in which direction to
move in search space, and it always
moves in the single direction.
3. It just like Depth first search
1.Introduction
2.Characteristics
.
1. It is a local search algorithm, that this
algorithm has only the knowledge of local
domain/problem and don’t have any
knowledge of the global /problem.
2. Greedy approach, that means it will
run only till the best move if it finds the
next move is not better then the current
move then it will be stop there.
3. No backtracking, if it moves to the next
node and it is not the best move then it
can not do backtrack from that node.
3.Algorithm
.
1. Evaluate the initial state.
2. Loop until a solution is found or there is no operator left.
I. select and apply a new state.
II. Evaluate the new state.
3. If goal then quit.
4. If next state is better than the current state then next state is the current
state
4.Example
.
1.A blind person is climbing the
hill and he wants to reach the top
of the hill.
2.He will walk until he feels the
surface is upward so that he will
stop when he feels the surface is
flat or going downward,
3.Means he reached the top of the
mountain. (means to reach the
top of the mountain is the goal
state).
5.Problems in Hill Climbing
.
1.Local Maximum:As we know that
hill climbing has the knowledge of only
local problem and don’t have the
knowledge of global problem
2.As same you can see in the figure it
will move until it finds the top height of
the mountain, it will stop when it finds
the downward
3.But the found top height is not the
global top height. So here hill climbing
algorithm will get a failure
5.Problems in Hill Climbing
.
• Flatten / Flat maximum: As we
can see in figure the flat surface so
it means that when the hill
climbing algorithm stop getting the
better move than the current move
it will stop there.
• Ridge:As we know that the Hill
Climbing algorithm can moves only
in one direction so if it faces the
problem like Ridge it will not work
in this situations
7.Applications
.
1.Network-Flow 2.Travelling Salesman problem3.
3.8-Queens problem 4.Integrated Circuit design,
THANKS FOR
ATTENTION

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Ai-Activity-1.pptx

  • 1. Priyadarshini JL College of Engineering Topic :Hill Climbing Algorithm Subject : Artificial Intelligence Name :Vedant More (68) Guide :Prof.Sonal Bawankule
  • 3. Defination.:Basically, Hill Climbing is variant of generate and test method 2.In which feedback from test procedure is used to help generator to decide that in which direction to move in search space, and it always moves in the single direction. 3. It just like Depth first search 1.Introduction
  • 4. 2.Characteristics . 1. It is a local search algorithm, that this algorithm has only the knowledge of local domain/problem and don’t have any knowledge of the global /problem. 2. Greedy approach, that means it will run only till the best move if it finds the next move is not better then the current move then it will be stop there. 3. No backtracking, if it moves to the next node and it is not the best move then it can not do backtrack from that node.
  • 5. 3.Algorithm . 1. Evaluate the initial state. 2. Loop until a solution is found or there is no operator left. I. select and apply a new state. II. Evaluate the new state. 3. If goal then quit. 4. If next state is better than the current state then next state is the current state
  • 6. 4.Example . 1.A blind person is climbing the hill and he wants to reach the top of the hill. 2.He will walk until he feels the surface is upward so that he will stop when he feels the surface is flat or going downward, 3.Means he reached the top of the mountain. (means to reach the top of the mountain is the goal state).
  • 7. 5.Problems in Hill Climbing . 1.Local Maximum:As we know that hill climbing has the knowledge of only local problem and don’t have the knowledge of global problem 2.As same you can see in the figure it will move until it finds the top height of the mountain, it will stop when it finds the downward 3.But the found top height is not the global top height. So here hill climbing algorithm will get a failure
  • 8. 5.Problems in Hill Climbing . • Flatten / Flat maximum: As we can see in figure the flat surface so it means that when the hill climbing algorithm stop getting the better move than the current move it will stop there. • Ridge:As we know that the Hill Climbing algorithm can moves only in one direction so if it faces the problem like Ridge it will not work in this situations
  • 9. 7.Applications . 1.Network-Flow 2.Travelling Salesman problem3. 3.8-Queens problem 4.Integrated Circuit design,