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slide 1 mae 552 heuristic optimization instructor: john eddy lecture #20 3/10/02 taguchis orthogonal arrays slide 2 anova now that we have a predicted optimum observation

slide 1 mae 552 heuristic optimization instructor: john eddy lecture #17 3/4/02 taguchis orthogonal arrays slide 2 s/n ratio why use the signal / noise ratio? given

slide 1 mae 552 heuristic optimization instructor: john eddy lecture #16 3/1/02 taguchis orthogonal arrays slide 2 roulette wheel selection implementation the roulette

slide 1 mae 552 heuristic optimization instructor: john eddy lecture #19 3/8/02 taguchis orthogonal arrays slide 2 anova now that we have determined the variation in performance

slide 1 mae 552 heuristic optimization instructor: john eddy lecture #18 3/6/02 taguchis orthogonal arrays slide 2 additive model slide 3 so from the previous result,

slide 1 mae 552 heuristic optimization lecture 8 february 8, 2002 slide 2 http://www.statslab.cam.ac.uk/~richard/tmp/mcp/java/anneal/ annealing.html slide 3 a b c start

slide 1 mae 552 heuristic optimization lecture 4 january 30, 2002 slide 2 http://coool.mines.edu/report/node3.html slide 3 basics of problem solving-evaluation function

slide 1 mae 552 heuristic optimization lecture 3 january 28, 2002 slide 2 types of optimization and optimization algorithms there are a many different types of optimization

slide 1 mae 552 heuristic optimization lecture 2 january 25, 2002 slide 2 the optimization problem is then: find values of the variables that minimize or maximize the

slide 1 mae 552 heuristic optimization lecture 6 february 4, 2002 slide 2 summary of traditional methods we have learned up to this point that traditional local

slide 1 mae 552 heuristic optimization lecture 24 march 20, 2002 topic: tabu search slide 2 tabu search modifications what happens if we come upon a very good solution

slide 1 mae 552 heuristic optimization lecture 23 march 18, 2002 topic: tabu search slide 2 http://unisci.com/stories/20021/0315023.htm slide 3 tabu search the tabu search

slide 1 mae 552 heuristic optimization lecture 26 april 1, 2002 topic:branch and bound slide 2 parallel and distributed branch-and- bound/a* algorithms a branch-and-bound

slide 1 mae 552 heuristic optimization instructor: john eddy lecture #30 4/15/02 neural networks slide 2 non-linearity: (in response to a question asked) 1 st well

slide 1 mae 552 heuristic optimization instructor: john eddy lecture #31 4/17/02 neural networks slide 2 references: neural networks a comprehensive foundation

slide 1 mae 552 heuristic optimization lecture 28 april 5, 2002 topic:chess programs utilizing tree searches slide 2 the material in this lecture is from www.howstuffworks.com

slide 1 mae 552 heuristic optimization instructor: john eddy lecture #15 2/27/02 bit manipulation slide 2 bitwise operations for use in ga if you decide to use binary encoding!!

slide 1 mae 552 heuristic optimization instructor: john eddy lecture #32 4/19/02 fuzzy logic slide 2 references: neurofuzzy adaptive modeling and control, martin brown

1. presentation on orthogonal array testing submitted to: dr. jagtar singh submitted by: atul ranjan kamini singh uttam kumar vipin kr. singh 2. contents: introduction.

western university scholarship@western digitized teses 1988 te vibration of beams and plates studied using orthogonal polynomials chan-soo kim follow this and additional