PDF-Iterative Improvement Search Hill Climbing Simulated Annealing WALKSAT and Genetic Algorithms

Moore Professor School of Computer Science Carnegie Mellon University wwwcscmueduawm awmcscmuedu 4122687599 brPage 2br brPage 3br moveset i brPage 4br brPage 5br brPage 6br brPage 7br Idea One Idea Two Idea Three brPage 8br i exp brPage 9br brPage 1. photodetachment. of anionic copper carbonyl clusters in argon matrices. Ryan M. Ludwig, Michael E. Goodrich, . David T. Moore. Chemistry Department, Lehigh University. 69. th. ISMS, Univ. Illinois Urbana-Champaign. 2010-2011. Andry. Pinto. Hugo Alves. Inês Domingues. Luís Rocha. Susana Cruz. Simulated Annealing. Introduction to Simulated Annealing (SA). Meta-Heuristic Concept. Historical Approach. SA Algorithm. Nidia C. Gallego. Robbie a. Meisner. Tim D. . Burchell. Oak Ridge National Laboratory. INGSM-13. Seattle, WA – September 15. , . 2013. Irradiation damage in graphite induces dimensional changes. Polygranular. Neighborhood. Hill Climbing. : Sample p points randomly in the neighborhood of the currently best . solution; determine the best solution of the n sampled points. If it is better than the . current solution, make it the new current solution and continue the search; otherwise, . Computations. K-means. Performance of K-Means. Smith Waterman is a non iterative case and of course runs fine. Matrix Multiplication . 64 cores. Square blocks Twister. Row/Col . decomp. Twister. Matthew Kelly. April 12, 2011. What is Annealing?. Slow cooling of a heated substance allows atoms to line themselves up, creating a stronger structure with minimum energy. Accelerating the cooling process produces a structure with more energy and fewer atoms optimally aligned.. :. Idea: . Always follow the best neighbor. Pros: . Easy to implement; performs well in convex spaces. Cons: . Gets stuck in local-maximum; lost in plateaus; ridges . Local-Maximum. Global-Maximum. Valley. Define . Iterative Patterns. …. Iterative Patterns follow a specific . RULE. .. Examples of Iterative Patterns:. 2, 4, 6, 8, 10, …. 2, 4, 8, 16, 32, …. 96, 92, 88, 84, 80, …. 625, 125, 25, 5, …. Neighborhood. Randomized Hill . Climbing. : Sample p points randomly in the neighborhood of the currently . best solution. ; determine the best solution of the n sampled points. If it is better than the . Chapter 3 covered problems that considered the whole search space and produced a sequence of actions leading to a goal.. Chapter 4 covers techniques (some developed outside of AI) that don’t try to cover the whole space and only the goal state, not the steps, are important. . MapReduce. Fei. . Teng. Doga Tuncay. Outline. Goal. Genetic Algorithm. Why . MapReduce. . Hadoop. /Twister. Performance Issues. References. Goal. Implement a genetic algorithm on Twister to prove that Twister is an ideal . Likelihood Methods in Ecology. Jan. 30 – Feb. 3, 2011. Rehovot. , Israel. Parameter Estimation. “The problem of . estimation. is of more central importance, (. than hypothesis testing. )... . for in almost all situations we know that the . AI: Representation and Problem Solving. Local Search. Instructors: Fei Fang & Pat Virtue. Slide credits: CMU AI, http://ai.berkeley.edu. Learning Objectives. Describe and implement the following local search algorithms. . Liliana Teodorescu. . Physics vs Computer Science/Engineering. P. hysics. . and . Computer Science/Engineering . have . long . established . mutually . beneficial links. Computer Science/Engineering to Physics.