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AI local search

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Renas Rekany
Renas Rekany

The document discusses local search algorithms for optimization problems, including hill climbing, simulated annealing, and Tabu search. Hill climbing performs a local search by iteratively moving to neighbor states with improved cost until a local optimum is reached. Simulated annealing allows some "bad" moves with decreasing probability to help escape local optima. Tabu search uses a tabu list to avoid getting stuck in cycles and encourages exploring new regions of the search space. These local search methods are suitable for problems where the solution is the goal state itself rather than the path to get there.

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Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 Problem Solving by Searching Search Methods : Local Search for Optimization Problems
Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 2 Traveling Salesman Person  Find the shortest Tour traversing all cities once.
Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 Local Search Algorithms 2. Informed Search Strategies (Heuristic Search):  A search strategy which searches the most promising branches of the state-space first can: (1) find a solution more quickly, (2) find solutions even when there is limited time available, (3) often find a better solution, since more profitable parts of the state-space can be examined, while ignoring the unprofitable parts.  A search strategy which is better than another at identifying the most promising branches of a search-space is said to be more informed. I. Hill climbing, Simulated Annealing, Tabu search. II. Best first search. III. Greedy search. IV. A* search.
Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 Local Search Algorithms  Hill Climbing,  Simulated Annealing,  Tabu Search
Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 5 Hill Climbing • Hill climbing search algorithm (also known as greedy local search) uses a loop that continually moves in the direction of increasing values (that is uphill). • It terminates when it reaches a peak where no neighbor has a higher value. • "Like climbing Everest in thick fog with amnesia"
Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 Hill Climbing Search
Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 7 Hill Climbing states evaluation
Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 8 Hill Climbing in Action … Cost States
Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 9 Hill Climbing Current Solution
Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 10 Hill Climbing Current Solution
Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 11 Hill Climbing Current Solution
Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 12 Hill Climbing Current Solution
Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 13 Hill Climbing Best Local Minimum Global Minimum
Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 14 Issues The Goal is to find GLOBAL optimum. 1. How to avoid LOCAL optima? 2. When to stop? 3. Climb downhill? When?
Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 15 Simulated Annealing  Key Idea: escape local maxima by allowing some "bad" moves but gradually decrease their frequency  Take some uphill steps to escape the local minimum  Instead of picking the best move, it picks a random move  If the move improves the situation, it is executed. Otherwise, move with some probability less than 1.  Physical analogy with the annealing process:  Allowing liquid to gradually cool until it freezes  The heuristic value is the energy, E  Temperature parameter, T, controls speed of convergence.
Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 16 Simulated Annealing  Basic inspiration: What is annealing? In metallurgy, annealing is the physical process used to temperature or harden metals or glass by heating them to a high temperature and then gradually cooling them, thus allowing the material to coalesce into a low energy crystalline state. Heating then slowly cooling a substance to obtain a strong crystalline structure.  Key idea: Simulated Annealing combines Hill Climbing with a random walk in some way that yields both efficiency and completeness.
Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 17 Simulated Annealing in Action … Cost States Best
Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 18 Simulated Annealing Cost States Best
Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 19 Simulated Annealing Cost States Best
Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 20 Simulated Annealing Cost States Best
Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 21 Simulated Annealing Cost States Best
Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 22 Simulated Annealing Cost States Best
Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 23 Simulated Annealing Cost States Best
Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 24 Simulated Annealing Cost States Best
Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 25 Simulated Annealing Cost States Best
Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 26 Simulated Annealing Cost States Best
Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 27 Simulated Annealing Cost States Best
Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 28 Simulated Annealing Cost States Best
Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 29 Simulated Annealing Cost States Best
Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 30 Simulated Annealing Cost States Best
Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 31 Simulated Annealing Cost States Best
Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 32 Simulated Annealing Cost States Best
Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 33 Simulated Annealing Cost States Best
Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 34 Simulated Annealing Cost States Best
Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 35 Simulated Annealing Cost States Best
Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 36 Simulated Annealing Cost States Best
Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 37 Simulated Annealing Cost States Best
Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 38 Simulated Annealing Cost States Best
Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 39 Simulated Annealing Cost States Best
Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 40 Tabu Search Algorithm  Start with an initial feasible solution  Initialize Tabu list  Generate a subset of neighborhood and find the best solution from the generated ones  If move is not in Tabu list then accept  Repeat from 3 until terminating condition
Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 41 Tabu Search: TS in Action … Cost States
Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 42 Tabu Search: TS Best
Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 43 Tabu Search: TS Best
Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 44 Tabu Search: TS Best
Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 45 Tabu Search: TS Best
Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 46 Tabu Search: TS Best
Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 47 Tabu Search: TS Best
Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 48 Tabu Search: TS Best
Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 49 Tabu Search: TS Best
Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 50 Tabu Search: TS Best
Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 51 Tabu Search: TS Best
Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 52 Tabu Search: TS Best
Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 53 Tabu Search: TS Best
Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 54 Tabu Search: TS Best
Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 55 Tabu Search: TS Best
Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 56 Tabu Search: TS Best
Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 57 Tabu Search: TS Best
Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 58 Tabu Search: TS Best
Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 59 Tabu Search: TS Best
Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 60 Tabu Search: TS Best
Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 61 Tabu Search: TS Best
Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 62 Tabu Search: TS Best
Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 63 Tabu Search: TS Best
Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 64 Summary  Local search methods keep small number of nodes in memory. They are suitable for problems where the solution is the goal state itself and not the path.  Hill climbing and simulated annealing are examples of local search algorithms.
Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 65 References Russell, S. J., Norvig, P., Canny, J. F., Malik, J. M., & Edwards, D. D. (2003). Artificial intelligence: a modern approach (Vol. 2). Upper Saddle River: Prentice hall.

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AI local search

  • 1. Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 Problem Solving by Searching Search Methods : Local Search for Optimization Problems
  • 2. Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 2 Traveling Salesman Person  Find the shortest Tour traversing all cities once.
  • 3. Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 Local Search Algorithms 2. Informed Search Strategies (Heuristic Search):  A search strategy which searches the most promising branches of the state-space first can: (1) find a solution more quickly, (2) find solutions even when there is limited time available, (3) often find a better solution, since more profitable parts of the state-space can be examined, while ignoring the unprofitable parts.  A search strategy which is better than another at identifying the most promising branches of a search-space is said to be more informed. I. Hill climbing, Simulated Annealing, Tabu search. II. Best first search. III. Greedy search. IV. A* search.
  • 4. Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 Local Search Algorithms  Hill Climbing,  Simulated Annealing,  Tabu Search
  • 5. Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 5 Hill Climbing • Hill climbing search algorithm (also known as greedy local search) uses a loop that continually moves in the direction of increasing values (that is uphill). • It terminates when it reaches a peak where no neighbor has a higher value. • "Like climbing Everest in thick fog with amnesia"
  • 6. Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 Hill Climbing Search
  • 7. Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 7 Hill Climbing states evaluation
  • 8. Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 8 Hill Climbing in Action … Cost States
  • 9. Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 9 Hill Climbing Current Solution
  • 10. Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 10 Hill Climbing Current Solution
  • 11. Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 11 Hill Climbing Current Solution
  • 12. Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 12 Hill Climbing Current Solution
  • 13. Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 13 Hill Climbing Best Local Minimum Global Minimum
  • 14. Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 14 Issues The Goal is to find GLOBAL optimum. 1. How to avoid LOCAL optima? 2. When to stop? 3. Climb downhill? When?
  • 15. Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 15 Simulated Annealing  Key Idea: escape local maxima by allowing some "bad" moves but gradually decrease their frequency  Take some uphill steps to escape the local minimum  Instead of picking the best move, it picks a random move  If the move improves the situation, it is executed. Otherwise, move with some probability less than 1.  Physical analogy with the annealing process:  Allowing liquid to gradually cool until it freezes  The heuristic value is the energy, E  Temperature parameter, T, controls speed of convergence.
  • 16. Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 16 Simulated Annealing  Basic inspiration: What is annealing? In metallurgy, annealing is the physical process used to temperature or harden metals or glass by heating them to a high temperature and then gradually cooling them, thus allowing the material to coalesce into a low energy crystalline state. Heating then slowly cooling a substance to obtain a strong crystalline structure.  Key idea: Simulated Annealing combines Hill Climbing with a random walk in some way that yields both efficiency and completeness.
  • 17. Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 17 Simulated Annealing in Action … Cost States Best
  • 18. Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 18 Simulated Annealing Cost States Best
  • 19. Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 19 Simulated Annealing Cost States Best
  • 20. Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 20 Simulated Annealing Cost States Best
  • 21. Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 21 Simulated Annealing Cost States Best
  • 22. Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 22 Simulated Annealing Cost States Best
  • 23. Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 23 Simulated Annealing Cost States Best
  • 24. Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 24 Simulated Annealing Cost States Best
  • 25. Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 25 Simulated Annealing Cost States Best
  • 26. Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 26 Simulated Annealing Cost States Best
  • 27. Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 27 Simulated Annealing Cost States Best
  • 28. Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 28 Simulated Annealing Cost States Best
  • 29. Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 29 Simulated Annealing Cost States Best
  • 30. Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 30 Simulated Annealing Cost States Best
  • 31. Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 31 Simulated Annealing Cost States Best
  • 32. Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 32 Simulated Annealing Cost States Best
  • 33. Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 33 Simulated Annealing Cost States Best
  • 34. Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 34 Simulated Annealing Cost States Best
  • 35. Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 35 Simulated Annealing Cost States Best
  • 36. Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 36 Simulated Annealing Cost States Best
  • 37. Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 37 Simulated Annealing Cost States Best
  • 38. Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 38 Simulated Annealing Cost States Best
  • 39. Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 39 Simulated Annealing Cost States Best
  • 40. Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 40 Tabu Search Algorithm  Start with an initial feasible solution  Initialize Tabu list  Generate a subset of neighborhood and find the best solution from the generated ones  If move is not in Tabu list then accept  Repeat from 3 until terminating condition
  • 41. Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 41 Tabu Search: TS in Action … Cost States
  • 42. Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 42 Tabu Search: TS Best
  • 43. Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 43 Tabu Search: TS Best
  • 44. Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 44 Tabu Search: TS Best
  • 45. Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 45 Tabu Search: TS Best
  • 46. Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 46 Tabu Search: TS Best
  • 47. Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 47 Tabu Search: TS Best
  • 48. Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 48 Tabu Search: TS Best
  • 49. Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 49 Tabu Search: TS Best
  • 50. Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 50 Tabu Search: TS Best
  • 51. Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 51 Tabu Search: TS Best
  • 52. Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 52 Tabu Search: TS Best
  • 53. Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 53 Tabu Search: TS Best
  • 54. Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 54 Tabu Search: TS Best
  • 55. Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 55 Tabu Search: TS Best
  • 56. Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 56 Tabu Search: TS Best
  • 57. Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 57 Tabu Search: TS Best
  • 58. Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 58 Tabu Search: TS Best
  • 59. Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 59 Tabu Search: TS Best
  • 60. Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 60 Tabu Search: TS Best
  • 61. Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 61 Tabu Search: TS Best
  • 62. Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 62 Tabu Search: TS Best
  • 63. Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 63 Tabu Search: TS Best
  • 64. Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 64 Summary  Local search methods keep small number of nodes in memory. They are suitable for problems where the solution is the goal state itself and not the path.  Hill climbing and simulated annealing are examples of local search algorithms.
  • 65. Renas R. Rekany Artificial Intelligence Nawroz University Keep Reading as long as you breathComSci: Renas R. Rekany Oct2016 65 References Russell, S. J., Norvig, P., Canny, J. F., Malik, J. M., & Edwards, D. D. (2003). Artificial intelligence: a modern approach (Vol. 2). Upper Saddle River: Prentice hall.