Lecture16-3-1-2002

# Lecture16-3-1-2002 - MAE 552 Heuristic Optimization...

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MAE 552 Heuristic Optimization Instructor: John Eddy Lecture #16 3/1/02 Taguchi’s Orthogonal Arrays

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Roulette wheel selection Implementation The roulette wheel can be constructed as follows. Calculate the total fitness for the population as the sum of the fitness of each member. = = n i i f F 1
Roulette wheel selection Implementation The roulette wheel can be constructed as follows. • Next, calculate selection probability p i for each member F f p i i = i = 1,n

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Roulette wheel selection Implementation The roulette wheel can be constructed as follows. • Then, calculate cumulative probability q k for each member i = 1,n = = i j j i p q 1
Roulette wheel selection The resulting values of q i will all lie in the range [0, 1]. To actually perform the selection: – sort the designs by increasing q i generate a random number r = U[0, 1] – select the first design with a q i higher than r. Repeat this step until your next population is full.

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Background Taguchi laid the foundation for his Robust Design approach in the 50’s and 60’s. Since then, the approach has been validated by years of successful application. What is Robust Design?
Background Robust Design (as presented here) is an engineering methodology for improving productivity during R & D so that high quality products can be produced quickly and at a low cost. How does this approach fit into the class of Heuristic Optimization methods?

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Background A focus of this approach is on generating information about how different design parameters affect performance under different usage conditions. Robust design enables an engineer to generate information necessary for decision- making with less (~half) experimental effort.
Background The 2 primary tasks performed in Robust Design are: 1. Measurement of Quality during design / development. We want a leading indicator of quality by which we can evaluate the effect of changing a particular design parameter on the products performance.

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Background 1. Efficient experimentation to find dependable information about the design parameters. It is essential to obtain dependable information about the design parameters so that design changes during manufacturing and customer use can be avoided. The information should be obtained with minimum time and recources.
Background So you can tell from the last few slides that we intend to alter some parameters to achieve some goal. This is the very

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## This note was uploaded on 07/09/2011 for the course MAE 522 taught by Professor Hacker during the Spring '10 term at SUNY Buffalo.

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Lecture16-3-1-2002 - MAE 552 Heuristic Optimization...

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