2. TABLE OF CONTENTS
WHAT IS HILL CLIMBING? 1
IMPLEMENTATION PROCEDURE 2
ILLUSTRATION OF ALGORITHM PROCEDURE WITH EXAMPLES 3
PROPERTIES OF HILL CLIMBING SEARCH ALGORITHM 4
MERITS AND DEMERITS OF HILL CLIMBING SEARCH ALGORITHM 5
CODE REPRESENTATIONS 6
3. HILL CLIMBING
ALGORITHM
Hill climbing is a heuristic search used
for mathematical optimization
problems in the field of Artificial
Intelligence.
Given a large set of inputs and a good
heuristic function, it tries to find
sufficiently good solution to the
problem.
4. IMPLEMENTATION
PROCEDURE
The hill climbing algorithm is a simple
optimization algorithm that uses an iterative
process to find the best solution. The algorithm
starts with an initial solution and then makes
small changes to it in the hopes of improving it
5. STEPS IN IMPLEMENTING THE
SEARCH ALGORITHM
INITIALIZATION
Starts with an initial solution within the
workspace
EVALUATION
Evaluate the quality of the current solution using an
objective function or fitness measure.
SELECTING A NEIGHBORING STATE
Apply an operator to the current state to select a neighboring
state within the loop.
EVALUATE THE NEW STATE
If the new state is the goal state, return success and exit. If it's
better than the current state, update the current state to this
new state. If it's not better, discard it and continue the loop.
CONTINUE ITERATING
Continue iterating until the solution state is reached or until
no new operators are available to be applied to the current
state.
6.
7. PROPERTIES OF HILL CLIMBING ALGORITHM
• Generate and Test Approach: This feature involves generating
neighboring solutions and evaluating their effectiveness, always
aiming for an upward move in the solution space.
• Follows Greedy Approach: Unlike other algorithms, Hill Climbing does
not revisit or reconsider previous decisions, persistently moving
forward in the quest for the optimal solution.
• No backtracking: Unlike other algorithms, Hill Climbing does not revisit
or reconsider previous decisions, persistently moving forward in the
quest for the optimal solution.
9. APPLICATIONS OF HILL CLIMBING
Marketing: It’s instrumental in solving the classic Traveling-Salesman
problems, optimizing sales routes, and reducing travel time. This
leads to more efficient sales operations and better resource
utilization.
Robotics: enhancing the performance and coordination of various
robotic components. This leads to more sophisticated and efficient
robotic systems performing complex tasks.
Game Theory: In AI-based gaming, the algorithm is pivotal in
developing sophisticated strategies identifying moves that maximize
winning chances or scores.
10. SIMPLE EXAMPLE OF HILL
CLIMBING
Finding the shortest path between a number of points
and places that must be visited is the goal of the
algorithmic problem known as the “traveling salesman
problem” (TSP). The input here is a 2D array of
coordinates of cities and the output is a list of
integers that indicates the numbers of cities in
order(starting from zero)
12. CONCLUSION
The Hill Climbing Algorithm, with its simple yet
effective approach, stands as an essential tool in AI. Its
adaptability across various domains highlights its
significance in AI and optimization. Despite its inherent
limitations, as AI continues to evolve, the role of this
algorithm in navigating complex problems remains
indispensable.
13. REFERENCES
• Dhondge, T. (2022, October 30). Hill Climbing
Algorithm in Python - AskPython. AskPython.
https://www.askpython.com/python/examples/hill-
climbing-algorithm-in-python
14. GROUP MEMBERS
• AGYAPONG SOLOMON
• AWORTWE FRANCIS JUNIOR
• CYRILINA BRADI
• SANTA MICHAEL
• AMEZUWOE DONNE
• FELIX SARFO KANTANKA
• HACKMAN RAYMOND
• APPIAH DAVID
• KONTOH JOSHUA OWUSU
