Hill Climbing Algorithm In Artificial Intelligence | Artificial Intelligence Tutorial | Simplilearn

2. What’s in it for you? What is Hill Climbing algorithm? Types of Hill Climbing algorithm Problems in Hill Climbing algorithm Uses of Hill Climbing algorithm Hill Climbing algorithm Features State and Space diagram

3. What is Hill Climbing Algorithm? Hill Climbing Algorithm is a local search algorithm which is used to find the peak of a mountain or the best solution to a problem by continuously moving in the direction of increasing elevation

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5. What is Hill Climbing Algorithm? Consider the elevation shown. Using Hill Climbing Algorithm, we will select a point to start at and move upwards until we reach the highest peak in a local area

6. What is Hill Climbing Algorithm? Consider the elevation shown. Using Hill Climbing Algorithm, we will select a point to start at and move upwards until we reach the highest peak in a local area

7. Hill Climbing Algorithm Flowchart The flowchart for Hill Climbing Algorithm is shown Select new solution X from neighborhood Evaluate X Select X as current solution Select current solution S Evaluate S Is S better than X? Yes No

8. What is Hill Climbing Algorithm? • Algorithm: • Select an initial point and evaluate for it • Loop until a solution is found or there are no new operators to be applied. Then: • Select and apply new operator • Select a new point. If: • Better than current state, then set new state • Not better, then try new operator

9. Hill Climbing Algorithm Features

10. Features of Hill Climbing Algorithm Hill Climbing Algorithm is a variation of generate and test algorithm which generates alternative courses of actions to see if they will solve the problem Generate and Test Alternative

11. Features of Hill Climbing Algorithm Hill Climbing Algorithm will always move in a direction which will give the best outcome. This is done to optimize cost Greedy Approach

12. Features of Hill Climbing Algorithm The algorithm cannot go back to its old path. It does not remember its previous state and cannot backtrack No Backtracking

13. State and Space Diagram

14. State and Space Diagram The state and space diagram is a representation of the Hill Climbing Algorithm which shows the various states of the algorithm and their corresponding cost function values Objective Function State Space

15. State and Space Diagram Global Maxima The Global Maxima represents the highest peak in the entire state space diagram. This region has the best cost function in the entire area Objective Function State Space

16. State and Space Diagram Local Maxima The Local Maxima is the highest peak in its neighborhood, but there exists a better state (Global Maxima). It has the best cost function in its area Objective Function State Space

17. State and Space Diagram Plateau A Plateau is flat region in space. The neighboring points in a plateau have the same cost function Objective Function State Space

18. State and Space Diagram The Shoulder is a plateau which has an upwards edge. It curves up a bit further on and leads to a higher elevation Shoulder Objective Function State Space

19. State and Space Diagram The Current State is the region of the State Space diagram where we are present during the search Current State Objective Function State Space

20. State and Space Diagram Plateau Current State Shoulder Global Maxima Local Maxima Objective Function State Space

21. Types of Hill Climbing Algorithm

22. Types of Hill Climbing Algorithm Simple Hill Climbing Algorithm It is the easiest variation of the algorithm. It examines neighboring points one at a time and selects the one which optimizes the current cost as the next state Select new solution X from neighborhood Evaluate X Select X as current solution Select current solution S Evaluate S Is S better than X? Yes No

23. Types of Hill Climbing Algorithm • Algorithm: • Select an initial point and evaluate. If it is goal state, then stop or set current state as initial state • Loop until a solution is found current state does not change Steepest-Ascent Hill Climbing In this variation, first all the neighboring points are examined and the point closest to the solution is taken as next state. This more time consuming

24. Types of Hill Climbing Algorithm • Algorithm: • Select random point and evaluate. If it is goal state, then stop or set current state as initial state, or select another point • Loop until a solution is found current state does not change Stochastic Hill Climbing This algorithm does not examine its neighbors before moving. It selects a random neighbor and decides weather to update it as current state or not

25. Problems in Hill Climbing Algorithm

26. Problems in Hill Climbing Algorithm Local Maxima The algorithm might arrive at a Local Maxima and take it to be the highest elevation. This can be solved by Backtracking. We can simply keep a list of promising paths for the algorithm to follow up on Local Maxima

27. Problems in Hill Climbing Algorithm Plateau Plateau All points on a plateau have the same cost, hence it is not possible to find best direction. Instead, we select a random point far away from current point to get over he plateau

28. Problems in Hill Climbing Algorithm Ridge Ridge A Ridge is an area which is higher that it’s surroundings but has a slope so it can’t be reached in a single move. By moving in different directions, we can overcome this problem

29. Uses of Hill Climbing Algorithm Hill Climbing Algorithm can be used for solving various problems such as Evaluation Problems, Robotic Co-ordination and Inductive Circuit Design Circuit Design Problems which require evaluation/ thinking Co-ordination between Robots

30. Uses of Hill Climbing Algorithm The algorithm can also be used to solve the Travelling Salesman problem in which, given a set of cities and the distance between the cities, we find the shortest route a salesman will have to take to visit each city B A (7,8) (5,4) C E (4,6) D (2,10) (1,5)

31. Hill Climbing Algorithm Demo

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