Genetic Algorithm
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Transcript Genetic Algorithm
Genetic Algorithm
Yu-Chi Ho
Jonathan T. Lee
Harvard University
Sep. 7, 2000
1
Outline
Intuition
The Algorithm
Issues
References
2
First Genetic Algorithm
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Intuition
Based on the theory of evolution:
survival of the fittest, “good”
parents produce “good” children
First introduced by John Holland
(1975)
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Brief Description
Code the designs as chromosomes
A population-based model that
produces new designs through
“reproduction”
A simulated “evolution”
N
5
Example
Traveling Salesman Problem
A design/chromosome, the traveling
order of the cities:
[a b c d f e]
a
b
f
c
e
N
d
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The Algorithm
Step 0: Randomly generate an initial
population.
Step 1: Select parents and “reproduce” the
next generation
Step 2: Evaluate the fitness of the new
generation
Step 3: Replace the old generation with the
new generation
Step 4: Repeat step 1 though 3 till iteration
T
N
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Selection
Designs with a better fitness value
have a better chance to be chosen
as a parent
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To insure that the good “information”
from the good parents is being passed
onto the next generation
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Reproduction
Cross-over
Pick the parents
[a b c d e f] and [b c d a f e]
Pick the cross-over point
[a b c d | e f] and [b c d a | f e]
Reproduce
[a b c d
e f] [b c d a e f]
[b c d a
f e] [a b c d f e]
Correction if necessary
N
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Reproduction (cont.)
Mutation
Pick the parent
[a b c d e f]
Pick the mutation point and value
[a b c d e f]: e c
Reproduce
[a b c d e f] [a b c d c f]
Correction if necessary
[a b c d c f] [a b e d c f]
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Issues
Pros:
Cons:
Robust
What is the best way to encode the
designs?
Requirement:
Evaluation function must be relatively
fast
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References
Whitley, D., “A Genetic Algorithm Tutorial,”
Statistics and Computing, Volume 4, pp.
65-85, 1994.
Holland, J., Adaptation in Natural and
Artificial Systems, University of Michigan
Press, 1975.
Heitkötter, J. and D. Beasley (Eds.), The
Hitch-Hiker's Guide to Evolutionary
Computation,
<http://alife.santafe.edu/~joke/encore/w
ww/Q1_1.htm>
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