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Lecture 16: 03/26/2007
Recall:
Standard (Simple) Genetic Algorithm (SGA)
•
Fixedsize
populations (n)
•
Individuals in population are fixedlength
(L) bit strings
•
Exogenous
fitness function—each bit string x has a fitness f(x)—assume that f(x)>0, all x
P(t)>P(t+1) (populations) [that is P(t) evolves to P(t+1)] via:
1.
Selection
of parents for P(t+1) from P(t)
2.
Generation of offspring from selected parents
Talked briefly about various selection
methods (fitnessprop, tournament, ranked, elitist.)
Generation of
offspring via crossover mutation
.
Crossover probability p
c
and mutation probability p
m
—p
c
~.75, p
m
~.01
and also different kinds of crossover (onepoint, twopoint, or uniform.)
Do some backofenvelope calculation regarding SGA with fitnessproportional selection
with onept.
crossover (w/ prob. P
c
), bitwise mutation (p
m
).
What is
fitnessprop. selection
?
A probabilistic
selection method that gives every
individual a nonzero
probability of being selected as a parent (ie. don’t just pick the best!) and doesn’t guarantee any
individual is a parent.
To get the nparents, draw from P9t); draw an individual xєP
sel
(x) proportional to
f(x) with replacement.
Fact
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 Spring '07
 DELCHAMPS
 Algorithms

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