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Unformatted text preview: 1 CMPSCI 383 September 23, 2011 Beyond Classical Search: Local Search 2 Today’s lecture • Local Search • Hillclimbing • Simulated annealing • Local beam search • Genetic algorithms • Genetic programming • Local search in continuous state spaces 3 Recall: Evaluating a search strategy • Completeness — Does it always find a solution if one exists? • Optimality — Does it find the best solution? • Time complexity • Space complexity 4 Example: Breadthfirst search • Complete? • Optimal? • Time • Space • Yes (if b finite) • Yes, if cost = 1 per step Not optimal in general • 1+b+b 2 +b 3 +...+b d +b(b d1) = O(b d+1 ) • O(b d+1 ) 5 Is O(b d+1 ) a big deal? 2 4 6 8 10 12 14 Depth .11 sec 11 sec 19 min 31 hours 129 days 35 years 3523 years Time 1 megabyte 106 megabytes 10 gigabytes 1 terabytes 101 terabytes 10 petabytes 1 exabyte Memory How can we ease up on completeness and optimality in the interest of improving time and space complexity ? 6 Local search algorithms • In many optimization problems, the path to the goal is irrelevant; the goal state itself is the solution • In such cases, we can use local search algorithms • keep a single “current” state, try to improve it 7 What is unique about local search? • Many search problems only require finding a goal state, not the path to that goal state • Examples • Actual physical (vs. virtual) navigation • Configuration problems — e.g., nQueens problem, determining good compiler parameter settings...
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This note was uploaded on 11/29/2011 for the course COMPSCI 383 taught by Professor Andrewbarto during the Fall '11 term at UMass (Amherst).
 Fall '11
 AndrewBarto
 Artificial Intelligence, Algorithms, C Programming

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