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CEE 5290/COM S 5722 Heuristic Methods for Optimization
Homework 6: Binary and Real Genetic Algorithm
Assigned: Friday, October 7, 2011
Due: Friday, October 14, 2011
TA office hours: Wed 10:0011:00am, Thu: 3:00 4:30pm in Hollister 203,
Prof. Shoemaker M(23), W & F (2:303:30), Rm 210 Hollister
Readings: Lecture handouts on Real Valued GA and constraint handling in GA; Optional
reading is Reed, P., B. Minsker, and D. E. Goldberg, 2000 Designing a competent simple
genetic algorithm for search and optimization.
Water Resources Research
, 36(12): 3757
3761.
Instructions on how to download this paper from the library are in the
Assignments folder under HW6 in Blackboard
.
1.
A 5bit binary GA with a population size of 4 is used to solve a maximization
problem. At generation zero the following information is available to you:
Member
String
Fitness
1
10001
20
2
11001
10
3
00101
5
4
01101
15
The following GA algorithm parameters hold:
Probability of crossover = 1
In each crossover operation a onepoint crossover strategy with 2 parents will be
employed to produce exactly one child. The first part of the genes of the child (bits 1 to
the crossover point) will come from the first parent, and the second part (from the
crossover point to the end of the string) from the second parent.
(a)
If roulette wheel selection is employed and if there is no mutation,What is the
chance that a member in generation 1 will be of the form 1xxxx? What is the
chance that a member in generation 1 will be of the form 0xxxx?
Now assume bitwise mutation probability, pMutation = 0.01
(b)
Now assume bitwise mutation probability, pMutation = 0.01 and no
crossover.What is the chance now that a member in generation 1 will be of the
form 1xxxx? What is the chance that a member in generation 1 will be of the form
0xxxx?
2.
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 Fall '11
 Shoemaker

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