Hill climbing is a local search algorithm that evaluates the current state and moves to a neighboring state that improves the value of the objective function. It continues moving to better states until a local optimum is reached. Problems with hill climbing include getting stuck at local optima rather than the global optimum, flat areas where no improving moves are possible, and ridges where it is limited to moving in one direction. Applications of hill climbing include network flow problems, the traveling salesman problem, and integrated circuit design.

1. Priyadarshini JL College
of Engineering
Topic :Hill Climbing Algorithm
Subject : Artificial Intelligence
Name :Vedant More (68)
Guide :Prof.Sonal Bawankule

2. CONTENT
1.Introduction
2.Characteristics
3.Algorithm
4.Example
5.Problems in Hill Climbing
6.Applications

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,

10. THANKS FOR
ATTENTION