MAE 552 Heuristic Optimization
Instructor: John Eddy Lecture #30 4/15/02 Neural Networks
Neural Networks
Non-Linearity: (In response to a question asked) 1st We'll define a non-linear system as one in which the outputs are not proportional to the inputs.
MAE 552 Heuristic Optimization Lecture 26 April 1, 2002 Topic:Branch and Bound
Branch and Bound
We have seen this semester that the size of real-world problems grows very large as the number of design variables increases. Recall that there are (n-1)!/2 d
MAE 552 Heuristic Optimization Lecture 25 March 22, 2002 Topic: Tabu Search
A Simple Illustration of Tabu Search
A Simple Version of the short term memory component of the Tabu Search is illustrated in this example. The problem is known as a minimum spann
MAE 552 Heuristic Optimization Lecture 24 March 20, 2002 Topic: Tabu Search
Tabu Search Modifications
What happens if we come upon a very good solution and pass it by because it is Tabu? Perhaps we should incorporate more flexibility into the search. May
MAE 552 Heuristic Optimization Lecture 23 March 18, 2002 Topic: Tabu Search
http:/unisci.com/stories/20021/0315023.htm
Tabu Search
The Tabu search begins by marching to a local minima.To avoid retracing the steps used, the method records recent moves in
MAE 552 Heuristic Optimization
Instructor: John Eddy Lecture #17 3/4/02 Taguchi's Orthogonal Arrays
S/N Ratio
Why use the signal / noise ratio?
Given a product or process with a target performance, deviation from that performance can typically be expres
MAE 552 Heuristic Optimization
Instructor: John Eddy Lecture #16 3/1/02 Taguchi's Orthogonal Arrays
Roulette wheel selection
Implementation
The roulette wheel can be constructed as follows.
Calculate the total fitness for the population as the sum of th
MAE 552 Heuristic Optimization
Instructor: John Eddy Lecture #14 2/25/02 Evolutionary Algorithms
Practical Implementation Issues
The next set of slides will deal with some issues encountered in the practical implementation of a genetic algorithm. Specific
MAE 552 Heuristic Optimization Lecture 5 February 1 , 2002
Traditional Search Methods
Exhaustive Search checks each and every solution in the search space until the solution has been found. Works well and is easy to code for small search problem. For larg
MAE 552 Heuristic Optimization Lecture 2 January 25, 2002
The optimization problem is then:
Find values of the variables that minimize or maximize the objective function while satisfying the constraints. The standard form of the constrained optimization p
MAE 552 Heuristic Optimization
Due Date: Friday Feb 8, 2002
Homework #1
Instructor: Dr. Kurt Hacker
Problem One
In a heuristic search algorithm, one of the primary goals is to balance the global and local
search components. Discuss how this balance is pro
MAE 552 Heuristic Optimization
Due Date: Friday Feb 8, 2002
Homework #1
Instructor: Dr. Kurt Hacker
Problem One
In a heuristic search algorithm, one of the primary goals is to balance the global and local
search components. Discuss how this balance is pro
MAE 552
Homework #3
Due Date: March 11, 2002
Genetic Algorithms
Minimize:
n
n
cos 4 ( x i ) 2 cos 2 ( x i )
F ( x ) = i =1
i =1
(1)
n
ixi2
i =1
Subject to:
n
g1 ( x )
0.75 xi 0
g 2 ( x)
xi
0<xi<10
i =1
n
15n
i =1
2
(2)
0
(3)
i = 1, n
(4)
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MAE 552 Optional Homework Assignment Due Date: Friday, April 26, 2002 NO EXTENTIONS WHATSOEVER. Taguchi's orthogonal arrays. Preamble: Consider a design problem for which you have no analytical means of calculating or accurately estimating the performance