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r
o
v
en
t
o
b
e
m
o
r
e
ef
f
icien
t
th
a
n
G
A
an
d
P
SO.
I
n
[
4
]
Z
h
ao
,
p
r
o
p
o
s
ed
a
h
y
b
r
id
g
e
n
etic
alg
o
r
it
h
m
f
o
r
B
ay
esia
n
n
et
w
o
r
k
o
p
tim
izatio
n
.
T
h
eir
w
o
r
k
u
s
ed
th
e
S
i
m
u
lated
An
n
ea
li
n
g
tec
h
n
o
lo
g
y
to
s
elec
t
c
h
ild
r
en
a
n
d
u
s
ed
s
elf
-
ad
ap
tiv
e
p
r
o
b
a
b
ilit
ies
o
f
cr
o
s
s
o
v
er
a
n
d
m
u
ta
tio
n
to
co
n
d
u
ct
t
h
e
lo
ca
l
s
ea
r
ch
.
Fi
n
all
y
,
t
h
e
Hill
-
cl
i
m
b
in
g
al
g
o
r
ith
m
w
a
s
e
m
p
lo
y
ed
to
o
p
ti
m
ize
th
e
r
es
u
lts
.
I
n
[
5
]
,
W
u
an
d
L
u
s
tu
d
ie
d
th
e
e
f
f
ec
t
s
o
f
h
y
b
r
id
o
p
ti
m
i
za
tio
n
s
tr
ate
g
ie
s
b
y
in
co
r
p
o
r
atin
g
th
e
m
etr
o
p
o
lis
ac
ce
p
tan
ce
cr
iter
io
n
o
f
Si
m
u
lat
ed
A
n
n
ea
li
n
g
(
S
A
)
in
t
o
th
e
cr
o
s
s
o
v
er
o
p
er
ato
r
o
f
GA
.
T
h
e
al
g
o
r
ith
m
w
as
u
s
ed
t
o
s
i
m
u
lta
n
eo
u
s
l
y
o
p
ti
m
ize
th
e
in
p
u
t
f
ea
tu
r
e
s
u
b
s
e
t
s
elec
t
io
n
,
th
e
t
y
p
e
o
f
k
er
n
el
f
u
n
ctio
n
an
d
th
e
k
er
n
el
p
ar
a
m
eter
s
ettin
g
o
f
SV
R
,
n
a
m
el
y
GAS
A
–
SV
R
.
I
n
s
u
m
m
ar
y
o
f
t
h
e
ab
o
v
e,
th
e
s
t
u
d
y
o
f
h
y
b
r
id
Ge
n
etic
A
l
g
o
r
ith
m
s
h
as
y
ield
ed
s
ev
er
al
s
u
cc
es
s
f
u
l
ap
p
r
o
ac
h
es.
T
h
e
A
r
tif
icia
l
I
m
m
u
n
e
S
y
s
te
m
(
A
I
S),
h
a
s
b
ee
n
s
t
u
d
ied
d
e
ep
ly
i
n
r
ec
en
t
y
ea
r
s
w
h
ic
h
is
a
class
o
f
b
io
lo
g
icall
y
i
n
s
p
ir
ed
co
m
p
u
tatio
n
p
ar
ad
ig
m
[
6
]
.
A
I
S
ap
p
r
o
ac
h
es
ar
e
u
s
ed
i
n
v
a
r
io
u
s
o
p
ti
m
iza
tio
n
ap
p
licatio
n
s
an
d
m
o
s
t
o
f
th
e
m
s
h
o
w
b
etter
ef
f
icie
n
c
y
in
co
m
p
ar
i
s
o
n
w
it
h
o
th
er
p
o
p
u
latio
n
b
ased
alg
o
r
ith
m
s
.
Var
io
u
s
A
I
S
m
o
d
els
s
u
ch
as
clo
n
al
s
elec
tio
n
,
i
m
m
u
n
e
n
e
t
w
o
r
k
s
,
a
n
d
n
e
g
ati
v
e
s
e
lectio
n
ar
e
also
u
s
ed
i
n
s
ev
er
al
ap
p
licatio
n
s
s
u
ch
as
o
p
ti
m
izatio
n
,
cl
u
s
ter
i
n
g
,
p
atter
n
r
ec
o
g
n
itio
n
a
n
d
a
n
o
m
al
y
d
e
tectio
n
.
I
n
g
en
er
al,
GA
a
n
d
A
I
S
h
a
v
e
b
ee
n
ad
o
p
t
e
d
as
o
p
tim
izer
s
i
n
th
e
b
in
ar
y
b
ase
w
h
ich
i
s
ca
teg
o
r
ized
as
NP
-
h
ar
d
.
Z
h
u
[
7
]
,
in
v
e
s
ti
g
ated
t
w
o
t
h
eo
r
ies
o
f
AI
S
w
h
ic
h
ar
e
clo
n
al
s
elec
tio
n
an
d
i
m
m
u
n
e
n
et
w
o
r
k
t
h
eo
r
y
,
an
d
in
te
g
r
ated
th
e
m
w
it
h
P
SO to
s
o
lv
e
t
h
e
j
o
b
s
ch
ed
u
lin
g
p
r
o
b
le
m
.
I
n
h
is
r
esear
ch
,
th
e
c
lo
n
al
s
elec
tio
n
th
eo
r
y
is
u
s
ed
to
s
et
u
p
th
e
f
r
a
m
e
w
o
r
k
w
h
ic
h
co
n
tain
s
th
e
p
r
o
ce
s
s
es
o
f
s
elec
tio
n
,
clo
n
i
n
g
,
h
y
p
er
m
u
ta
tio
n
a
n
d
r
ec
ep
to
r
ed
itin
g
,
w
h
ile
t
h
e
i
m
m
u
n
e
n
et
w
o
r
k
t
h
eo
r
y
i
s
ap
p
lied
to
in
cr
ea
s
e
t
h
e
d
i
v
er
s
it
y
o
f
t
h
e
p
o
ten
t
ial
s
o
l
u
tio
n
r
ep
er
to
ir
e.
B
ar
an
i
[
8
]
,
p
r
o
p
o
s
ed
an
ap
p
r
o
ac
h
b
ased
o
n
th
e
g
en
et
ic
alg
o
r
ith
m
(
GA
)
an
d
ar
tific
ial
i
m
m
u
n
e
s
y
s
te
m
(
A
I
S),
ca
lled
GAA
I
S,
f
o
r
d
y
n
a
m
ic
i
n
tr
u
s
io
n
d
etec
tio
n
i
n
AODV
-
b
ased
MA
NE
T
s
.
His
ap
p
r
o
ac
h
w
a
s
ab
le
to
ad
ap
t
its
elf
to
n
et
w
o
r
k
to
p
o
lo
g
y
c
h
a
n
g
e
s
u
s
in
g
t
w
o
u
p
d
ati
n
g
m
eth
o
d
s
:
p
ar
tial
an
d
to
tal.
E
ac
h
n
o
r
m
al
f
ea
t
u
r
e
v
ec
to
r
ex
tr
ac
ted
f
r
o
m
n
e
t
w
o
r
k
tr
a
f
f
ic
w
a
s
r
ep
r
esen
ted
b
y
a
h
y
p
er
s
p
h
er
e
w
ith
f
ix
r
ad
i
u
s
.
A
l
i
et
a
l
[
9
]
,
i
m
p
r
o
v
ed
th
e
r
esu
lt
s
o
f
p
er
f
o
r
m
a
n
ce
in
t
h
e
h
y
b
r
id
A
I
S
an
d
G
A
.
T
h
e
h
y
b
r
id
in
clu
d
ed
t
w
o
p
r
o
ce
s
s
e
s
;
f
ir
s
tl
y
,
A
I
S
e
n
ab
les
it
to
d
ev
elo
p
lo
ca
l
s
ea
r
ch
in
g
a
b
ilit
y
a
n
d
ef
f
icie
n
c
y
a
lth
o
u
g
h
th
e
co
n
v
er
g
e
n
ce
r
ate
f
o
r
A
I
S
is
p
r
ef
er
ab
l
y
n
o
t
p
r
ec
is
e
co
m
p
ar
ed
to
th
e
GA
.
Seco
n
d
l
y
,
a
Gen
etic
A
lg
o
r
it
h
m
i
s
t
y
p
icall
y
in
it
ializi
n
g
p
o
p
u
latio
n
r
an
d
o
m
l
y
.
T
h
e
last
g
en
er
atio
n
o
f
A
I
S
w
i
ll
b
e
th
e
in
p
u
t
to
th
e
n
e
x
t
p
r
o
ce
s
s
o
f
t
h
e
h
y
b
r
id
w
h
ic
h
is
t
h
e
GA
i
n
t
h
is
h
y
b
r
id
A
I
S
-
G
A
.
A
h
y
b
r
id
ca
n
en
s
u
r
e
th
at
a
GA
e
n
ter
s
t
h
e
s
tag
e
o
f
s
tan
d
ar
d
s
o
lu
tio
n
s
m
o
r
e
r
ap
id
ly
a
n
d
ac
cu
r
atel
y
co
m
p
ar
ed
to
GA
i
n
it
ialized
p
o
p
u
latio
n
at
r
an
d
o
m
.
As
m
e
n
tio
n
ab
o
v
e,
t
h
e
h
y
b
r
id
A
I
S
an
d
G
A
h
a
v
e
b
ee
n
ap
p
lied
to
d
if
f
er
en
ce
o
p
ti
m
izatio
n
a
p
p
licatio
n
ar
ea
s
in
r
ec
en
t
y
ea
r
s
.
T
h
e
o
b
j
ec
t
o
f
t
h
i
s
p
ap
er
is
to
d
escr
ib
e
th
e
m
o
d
if
ied
Gen
e
tic
al
g
o
r
ith
m
(
G
A
)
w
h
ic
h
i
s
a
co
m
b
i
n
atio
n
o
f
an
A
r
tific
ial
I
m
m
u
n
e
S
y
s
te
m
(
A
I
S)
to
f
o
r
m
an
I
m
m
u
n
e
Gen
etic
A
l
g
o
r
ith
m
(
I
G
A
)
to
r
ed
u
ce
th
e
s
ea
r
c
h
s
p
ac
e
an
d
ac
h
ie
v
e
ef
f
i
cien
t
s
ea
r
ch
e
s
.
P
er
f
o
r
m
an
ce
s
o
f
t
h
e
I
G
A
an
d
t
w
o
o
t
h
er
tech
n
iq
u
es
w
ill
b
e
co
m
p
ar
ed
.
T
h
is
p
ap
er
is
d
i
v
i
d
ed
as
f
o
llo
w
s
:
Sectio
n
2
p
r
esen
t
s
t
h
e
r
esear
c
h
m
et
h
o
d
o
f
t
h
e
e
v
o
lu
t
io
n
ar
y
alg
o
r
ith
m
.
Sectio
n
3
co
v
er
s
th
e
r
esu
lts
a
n
d
an
al
y
s
i
s
.
Fi
n
all
y
,
th
e
co
n
cl
u
s
io
n
w
i
ll b
e
p
r
esen
t
ed
in
s
ess
io
n
4
.
2.
RE
S
E
ARCH
M
E
T
H
O
D
T
h
is
s
ec
tio
n
d
is
cu
s
s
e
s
a
n
d
an
al
y
ze
s
th
e
a
i
m
o
f
t
h
e
h
y
b
r
id
i
m
m
u
n
e
g
e
n
etic
al
g
o
r
ith
m
co
n
ce
p
ts
to
u
tili
ze
t
h
e
lo
ca
ll
y
c
h
ar
ac
ter
is
tic
i
n
f
o
r
m
atio
n
to
s
ee
k
o
u
t
t
h
e
w
a
y
s
a
n
d
m
ea
n
s
o
f
d
is
co
v
er
in
g
th
e
o
p
ti
m
al
s
o
lu
tio
n
w
h
e
n
d
ea
lin
g
w
ith
d
i
f
f
icu
l
t
p
r
o
b
lem
s
.
O
n
e
m
u
s
t
f
ir
s
t
g
e
n
er
ate
a
r
an
d
o
m
d
etec
to
r
,
an
d
th
en
th
e
i
n
itial
p
o
p
u
latio
n
.
Nex
t p
er
f
o
r
m
s
ele
ctio
n
,
cr
o
s
s
o
v
er
,
an
d
m
u
tat
io
n
u
p
o
n
th
e
p
o
p
u
latio
n
f
o
r
a
n
u
m
b
er
o
f
g
e
n
er
atio
n
s
,
u
n
t
il ter
m
in
atio
n
cr
iter
io
n
is
m
et
.
2
.
1.
Ne
g
a
t
iv
e
Select
io
n
Neg
ati
v
e
s
elec
t
io
n
i
n
s
p
ir
ed
f
r
o
m
t
h
e
T
ce
ll
m
at
u
r
atio
n
p
r
o
ce
s
s
h
a
s
b
ee
n
d
ev
elo
p
ed
f
o
r
s
e
lf
-
n
o
n
s
el
f
d
etec
tio
n
in
co
m
p
u
ter
s
y
s
te
m
s
.
I
n
th
i
s
tech
n
iq
u
e,
t
h
e
f
ir
s
t
i
n
f
o
r
m
atio
n
i
s
r
ep
r
esen
ted
in
a
s
u
itab
le
f
o
r
m
s
u
ch
as
s
tr
i
n
g
f
o
r
m
,
r
ea
l
v
al
u
ed
v
ec
to
r
f
o
r
m
,
a
n
d
h
y
b
r
id
f
o
r
m
ar
e
co
n
s
id
er
ed
as
s
elf
-
d
ata.
T
h
en
ad
d
itio
n
al
d
ata
ar
e
cr
ea
ted
in
th
e
s
a
m
e
f
o
r
m
as
t
h
e
s
elf
-
d
ata,
i
n
s
u
ch
a
w
a
y
th
at
an
y
o
f
t
h
e
n
e
w
l
y
cr
ea
ted
d
ata
d
o
es
n
o
t
m
atc
h
t
h
e
s
elf
-
d
ata.
T
h
e
m
atc
h
in
g
i
s
d
o
n
e
ac
co
r
d
in
g
to
a
m
atc
h
in
g
r
u
le
w
h
ic
h
i
s
s
e
lecte
d
d
ep
en
d
in
g
o
n
s
u
itab
ilit
y
.
T
h
ese
n
e
w
l
y
cr
ea
ted
d
ata
wh
ich
ar
e
u
s
ed
to
d
i
s
ti
n
g
u
i
s
h
b
et
w
ee
n
s
el
f
-
d
ata
an
d
n
o
n
s
elf
-
d
ata
ar
e
ca
lled
d
etec
to
r
s
.
I
f
an
y
o
f
t
h
e
d
etec
to
r
s
m
a
tch
e
s
th
e
d
ata,
th
e
n
th
a
t
d
ata
is
co
n
s
id
er
ed
n
o
n
s
el
f
-
d
ata.
W
h
er
ea
s
,
if
n
o
de
tecto
r
m
atch
e
s
t
h
e
d
ata
th
e
n
t
h
at
d
ata
is
co
n
s
id
er
ed
s
elf
-
d
ata.
T
h
e
d
etec
to
r
s
ar
e
cr
ea
ted
in
s
u
c
h
a
w
a
y
t
h
a
t
th
e
y
d
o
n
o
t
m
atc
h
an
y
o
f
t
h
e
s
elf
-
d
ata
.
I
n
n
eg
at
iv
e
s
elec
tio
n
,
th
e
T
ce
ll
is
p
r
esen
ted
to
t
h
e
s
elf
-
b
o
d
y
ce
lls
.
I
f
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
E
C
E
I
SS
N:
2
0
8
8
-
8708
Th
e
E
va
lu
a
ted
Mea
s
u
r
eme
n
t o
f a
C
o
mb
in
ed
Gen
etic
A
lg
o
r
ith
m
a
n
d
A
r
tifi
cia
l …
.
(
P
o
n
g
s
a
r
u
n
B
o
o
n
yo
p
a
ko
r
n
)
2073
th
e
T
ce
ll
r
ec
o
g
n
izes
an
y
o
f
t
h
e
s
el
f
-
b
o
d
y
ce
lls
,
t
h
en
t
h
e
ce
ll
is
r
ej
ec
ted
.
R
e
m
ain
in
g
T
c
ells
ar
e
co
n
s
id
er
ed
m
at
u
r
ed
T
ce
lls
an
d
ar
e
u
s
ed
f
o
r
th
e
s
elf
-
n
o
n
s
e
lf
d
etec
tio
n
[
1
0
]
.
Pse
u
d
o
c
o
d
e
f
o
r
d
e
t
e
c
t
o
r
g
e
n
e
r
a
t
i
o
n
1:
In
p
u
t
:
S
e
l
f
D
a
t
a
2:
Ou
t
p
u
t
:
Re
p
e
r
t
o
i
re
3:
R
e
p
e
r
t
o
i
r
e
←Φ
4:
W
h
i
l
e
(
¬
S
t
o
p
C
o
n
d
i
t
i
o
n
(
)
)
5:
D
e
t
e
c
t
o
r
s
←
G
e
n
e
r
a
t
e
R
a
n
d
o
mD
e
t
e
c
t
o
r
s(
)
6:
Fo
r
(
D
e
t
e
c
t
o
r
i
R
e
p
e
rt
o
i
re
)
7:
If
(
N
o
t
M
a
t
c
h
e
s(
D
e
t
e
c
t
o
r
i
,
S
e
l
f
D
a
t
a
))
8:
R
e
p
e
rt
o
i
r
e
←
D
e
t
e
c
t
o
r
i
9:
E
n
d
10:
E
n
d
11:
E
nd
12:
R
e
t
u
r
n
(
Re
p
e
r
t
o
i
r
e
)
Fig
u
r
e
1
.
P
s
eu
d
o
co
d
e
f
o
r
d
etec
to
r
g
en
er
atio
n
Fig
u
r
e
1
d
escr
ib
e
s
t
h
e
m
aj
o
r
s
tep
s
i
n
s
u
c
h
a
n
al
g
o
r
ith
m
.
I
n
t
h
e
g
en
er
at
io
n
s
tag
e,
th
e
d
etec
to
r
s
ar
e
g
en
er
ated
b
y
a
f
e
w
r
an
d
o
m
p
r
o
ce
s
s
an
d
ce
n
s
o
r
ed
b
y
tr
y
i
n
g
to
m
atc
h
s
el
f
s
a
m
p
les.
T
h
o
s
e
ca
n
d
id
ates
t
h
at
m
atc
h
ar
e
eli
m
in
a
ted
an
d
th
e
r
est
ar
e
k
ep
t
as
d
etec
to
r
s
.
I
n
t
h
e
d
etec
tio
n
s
tag
e,
t
h
e
co
llectio
n
o
f
d
etec
to
r
s
(
o
r
d
etec
to
r
s
et)
ar
e
u
s
ed
to
ch
ec
k
w
h
e
th
er
an
i
n
co
m
i
n
g
d
ata
in
s
tan
ce
is
s
el
f
o
r
n
o
n
s
el
f
.
I
f
it
m
atch
es
a
n
y
d
etec
to
r
(
r
ef
er
ed
to
Fi
g
u
r
e
2
)
,
it
is
clai
m
ed
as
n
o
n
s
el
f
o
r
an
an
o
m
al
y
.
T
h
is
d
escr
ip
tio
n
is
li
m
ited
to
a
f
e
w
e
x
te
n
t
s
,
b
u
t
co
n
v
e
y
s
t
h
e
ess
e
n
tia
l id
ea
.
Pse
u
d
o
c
o
d
e
f
o
r
d
e
t
e
c
t
o
r
a
p
p
l
i
c
a
t
i
o
n
1:
In
p
u
t
:
I
n
p
u
t
S
a
m
p
l
e
s
,
R
e
p
e
r
t
o
i
r
e
2:
Fo
r
(
I
n
p
u
t
i
c
l
as
s
I
n
p
u
t
S
a
m
p
l
e
s
)
3:
I
n
p
u
t
i
c
l
as
s
←
"
n
o
n
-
se
l
f
"
4:
Fo
r
(
D
e
t
e
c
t
o
r
i
R
e
p
e
r
t
o
i
r
e
)
5:
If
(
M
a
t
c
h
e
s
(
I
n
p
u
t
i
,
D
e
t
e
c
t
o
r
i
))
6:
I
n
p
u
t
i
c
l
as
s
←
"
se
l
f
"
7:
B
r
e
a
k
8:
E
n
d
9:
E
n
d
10:
E
n
d
Fig
u
r
e
2
.
P
s
eu
d
o
co
d
e
f
o
r
d
etec
to
r
ap
p
licatio
n
2
.
2.
M
a
t
ching
Rules
Ma
tch
i
n
g
r
u
le
is
a
n
i
m
p
o
r
tan
t
p
ar
t
in
d
etec
to
r
g
en
er
atio
n
.
T
h
er
e
ar
e
d
if
f
er
en
t
m
atch
in
g
r
u
le
s
s
u
c
h
a
s
Ha
m
m
i
n
g
d
is
ta
n
ce
,
B
in
ar
y
d
is
tan
ce
,
E
d
it
d
is
ta
n
ce
,
a
n
d
V
alu
e
d
i
f
f
er
en
ce
m
etr
ic
to
m
a
tch
s
tr
in
g
s
.
I
n
th
is
p
ap
er
,
f
o
cu
s
is
o
n
th
e
R
-
C
o
n
t
ig
u
o
u
s
B
it
s
(
R
C
B
)
m
atc
h
i
n
g
r
u
le
an
d
R
-
C
h
u
n
k
m
atch
in
g
r
u
le
[
1
1
]
.
T
h
e
R
C
B
m
atc
h
in
g
r
u
le
is
d
ef
i
n
ed
as
f
o
llo
w
s
:
I
f
x
a
n
d
y
is
eq
u
al
-
le
n
g
t
h
s
tr
i
n
g
s
d
e
f
i
n
ed
o
v
er
a
f
i
n
ite
a
lp
h
ab
et,
m
atc
h
(
x
,
y
)
is
tr
u
e
if
x
an
d
y
ag
r
ee
in
at
least
r
co
n
tig
u
o
u
s
lo
ca
ti
o
n
s
.
As
in
th
e
R
C
B
m
atc
h
i
n
g
r
u
le,
a
d
etec
to
r
is
s
p
ec
if
ied
b
y
a
b
in
ar
y
s
tr
i
n
g
c
a
n
d
p
ar
am
e
ter
r
.
2
.
3.
Det
ec
t
o
r
G
ener
a
t
io
n
T
h
e
d
etec
to
r
g
en
er
atio
n
tec
h
n
iq
u
e
ca
n
b
e
d
i
v
id
ed
in
to
t
w
o
p
ar
ts
.
i)
T
h
e
v
al
u
e
o
f
t
h
e
len
g
th
o
f
t
h
e
ch
u
n
k
is
ta
k
en
f
r
o
m
t
h
e
u
s
er
.
L
et
t
h
e
ch
u
n
k
le
n
g
th
be
x
th
e
n
f
r
o
m
t
h
e
f
ir
s
t
b
it
o
f
a
s
el
f
-
s
t
r
in
g
x
,
no
ne
o
f
t
h
e
co
n
tin
u
o
u
s
b
it
s
ar
e
ta
k
en
to
f
o
r
m
a
c
h
u
n
k
.
T
h
en
to
f
o
r
m
t
h
e
s
ec
o
n
d
b
it
x
,
no
ne
of
th
e
co
n
tin
u
o
u
s
b
it
s
ar
e
tak
en
fo
r
m
an
o
t
h
er
c
h
u
n
k
an
d
th
is
g
o
es
o
n
as
lo
n
g
as
x
h
a
s
no
ne
o
f
t
h
e
co
n
tin
u
o
u
s
b
its
tak
e
n
to
f
o
r
m
a
c
h
u
n
k
.
So
,
if
th
e
len
g
t
h
o
f
s
el
f
-
s
tr
in
g
is
y
,
th
en
y
-
x
+1
no
ne
of
t
h
e
ch
u
n
k
s
ar
e
f
o
r
m
ed
f
r
o
m
ea
ch
s
elf
-
s
tr
i
n
g
.
ii
)
E
ac
h
s
elf
-
c
h
u
n
k
s
et
i
s
tak
e
n
o
n
e
b
y
o
n
e
to
t
h
e
d
etec
to
r
s
ets
s
ep
ar
atel
y
.
As
c
h
u
n
k
s
ar
e
alr
ea
d
y
cr
ea
ted
f
r
o
m
s
el
f
-
s
tr
in
g
s
t
w
o
s
tr
i
n
g
s
ar
e
co
n
s
id
er
ed
th
e
s
a
m
e
o
n
l
y
i
f
all
t
h
e
b
its
o
f
th
e
t
w
o
s
tr
in
g
s
ex
ac
tl
y
m
a
tch
ea
c
h
o
th
er
.
Nex
t,
d
etec
to
r
s
ar
e
to
b
e
cr
ea
ted
s
u
c
h
th
a
t
n
e
w
l
y
cr
ea
te
d
d
etec
to
r
s
d
o
n
o
t
m
atc
h
p
r
ev
io
u
s
l
y
g
e
n
er
ated
d
etec
to
r
s
o
r
th
e
s
elf
-
ch
u
n
k
s
tr
in
g
s
e
v
en
-
t
h
o
u
g
h
th
e
r
an
d
o
m
n
e
s
s
o
f
t
h
e
d
etec
to
r
g
en
e
r
atio
n
p
r
o
ce
s
s
ar
e
m
ai
n
tai
n
ed
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
J
E
C
E
Vo
l.
7
,
No
.
4
,
A
u
g
u
s
t
2
0
1
7
:
2
0
7
1
–
2
0
8
4
2074
2
.
4
.
Chro
m
o
s
o
m
e
Repre
s
ent
a
t
io
n
T
h
e
ch
r
o
m
o
s
o
m
e
r
ep
r
esen
tat
i
o
n
d
ep
en
d
s
o
n
th
e
n
at
u
r
e
o
f
t
h
e
p
r
o
b
lem
v
ar
iab
les.
T
h
e
v
al
u
e
o
f
a
b
it
s
tr
in
g
ca
n
b
e
an
i
n
te
g
er
n
u
m
b
er
o
r
b
in
ar
y
n
u
m
b
er
.
Fo
r
ex
a
m
p
le,
t
h
e
r
ep
r
esen
tatio
n
c
h
o
ice
o
f
ti
m
etab
lin
g
s
ch
ed
u
les
f
o
r
a
f
e
w
o
b
j
ec
ts
.
A
p
o
s
s
ib
le
n
u
m
b
er
o
f
1
5
-
b
it
s
tr
in
g
s
ca
n
b
e
u
s
ed
to
r
ep
r
esen
t
a
p
o
s
s
ib
le
s
o
lu
tio
n
to
a
p
r
o
b
lem
.
I
n
t
h
i
s
ca
s
e
b
its
o
r
s
u
b
s
ets
o
f
b
its
m
i
g
h
t
r
ep
r
esen
t
a
c
h
o
ice
o
f
a
f
e
w
f
ea
t
u
r
e
s
:
s
u
b
j
ec
t,
s
ec
tio
n
,
in
s
tr
u
cto
r
,
ti
m
e,
a
n
d
r
o
o
m
.
Fi
g
u
r
e
3
s
h
o
w
s
t
h
e
c
h
r
o
m
o
s
o
m
e
R
ep
r
esen
tat
io
n
1
1
0
0
1
1
1
0
0
1
1
0
0
1
1
Fig
u
r
e
3
.
C
h
r
o
m
o
s
o
m
e
R
ep
r
es
en
tatio
n
w
h
er
e
b
it 1
-
3
r
ep
r
esen
ts
s
u
b
j
ec
t,
4
-
6
r
ep
r
esen
ts
co
u
r
s
e
s
ec
ti
o
n
,
7
-
9
r
ep
r
esen
ts
in
s
tr
u
c
to
r
o
r
p
r
o
f
ess
o
r
,
1
0
-
12
r
ep
r
esen
ts
ti
m
es,
a
n
d
1
3
-
1
5
r
e
p
r
esen
ts
r
o
o
m
.
2
.
5
.
I
nitia
l P
o
pu
la
t
io
n
T
h
e
ch
r
o
m
o
s
o
m
e
’
s
f
it
n
es
s
v
a
lu
e
is
a
s
s
e
s
s
ed
d
u
r
i
n
g
th
e
i
n
it
ial
p
o
p
u
latio
n
p
r
o
ce
s
s
.
E
ac
h
i
n
d
iv
id
u
al
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n
e
x
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g
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er
atio
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o
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th
e
b
es
t
in
d
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v
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a
l.
I
t
s
to
ch
as
ticall
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a
llo
ca
tes
a
h
ig
h
er
n
u
m
b
er
o
f
co
p
ies
in
th
e
f
o
llo
w
in
g
g
e
n
er
atio
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to
h
ig
h
l
y
f
itt
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g
s
tr
i
n
g
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i
n
th
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ese
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t
g
e
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er
atio
n
.
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u
r
co
m
m
o
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m
et
h
o
d
s
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o
r
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ar
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R
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n
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m
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m
etr
ic
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tio
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a
n
d
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o
u
r
n
a
m
e
n
t
s
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n
.
Fo
r
ex
a
m
p
le
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F
ig
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r
e
4
s
h
o
w
s
T
o
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r
n
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m
e
n
t
s
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tio
n
w
h
ic
h
p
r
o
v
id
es
a
ch
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ce
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all
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d
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v
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d
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als
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d
th
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s
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er
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iv
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it
y
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h
o
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g
h
k
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p
in
g
d
iv
er
s
it
y
m
a
y
d
e
g
r
ad
e
th
e
co
n
v
er
g
e
n
ce
s
p
ee
d
.
I
n
to
u
r
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a
m
e
n
t
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e
lecti
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iv
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al
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ar
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o
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l
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r
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er
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o
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u
latio
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a
n
d
th
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in
d
iv
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als
co
m
p
ete
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g
ain
s
t
ea
ch
o
t
h
er
.
T
h
e
in
d
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v
id
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al
w
it
h
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e
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g
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e
s
t
f
it
n
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s
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n
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an
d
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ill
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e
in
cl
u
d
ed
as
o
n
e
o
f
th
e
n
e
x
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g
e
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er
atio
n
p
o
p
u
latio
n
.
T
h
e
n
u
m
b
er
o
f
in
d
i
v
id
u
a
ls
co
m
p
eti
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g
in
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ch
to
u
r
n
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m
e
n
t
is
r
ef
er
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ed
to
as
to
u
r
n
a
m
e
n
t
s
ize,
co
m
m
o
n
l
y
s
et
to
2
(
also
ca
lled
b
in
ar
y
to
u
r
n
a
m
en
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.
Fig
u
r
e
4
.
I
llu
s
tr
atio
n
o
f
T
o
u
r
n
a
m
en
t Sele
ctio
n
in
S
ize
o
f
2
2
.
7
.
Cro
s
s
o
v
er
T
h
e
cr
o
s
s
o
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er
p
r
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ce
s
s
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r
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ce
s
b
etter
c
h
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o
m
o
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o
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e
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t
w
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o
f
t
h
e
s
tr
o
n
g
est
ar
e
p
ick
ed
to
p
r
o
d
u
ce
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n
e
w
ch
r
o
m
o
s
o
m
e
o
f
o
f
f
s
p
r
i
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g
.
Fi
g
u
r
e
5
s
h
o
w
s
th
e
e
x
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m
p
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o
f
s
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er
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T
h
r
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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Mea
s
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ith
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u
r
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5
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llu
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o
f
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o
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g
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r
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er
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tatio
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n
a
l
r
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d
o
m
alter
atio
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o
f
a
v
alu
e
o
f
a
s
tr
in
g
p
o
s
itio
n
.
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h
e
p
u
r
p
o
s
e
o
f
m
u
tatio
n
in
G
As
ar
e
to
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r
eser
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e
a
n
d
i
n
tr
o
d
u
c
e
d
iv
er
s
it
y
.
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r
d
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f
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er
en
t
g
e
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o
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e
t
y
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e
s
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d
if
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er
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t
m
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y
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e
B
it
s
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g
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u
tatio
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,
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lip
B
it
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o
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n
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ar
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n
i
f
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m
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o
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h
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s
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o
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m
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u
r
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6
s
h
o
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m
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le
o
f
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it st
r
in
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tatio
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Fig
u
r
e
6
.
I
llu
s
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o
f
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it Str
in
g
M
u
tat
io
n
2
.
9
.
T
he
P
s
eu
do
co
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f
t
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Alg
o
r
it
h
m
T
h
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
b
e
g
i
n
s
w
it
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in
i
tialize
d
etec
to
r
D
,
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ch
o
f
w
h
ic
h
f
ails
to
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e
a
r
an
d
o
m
v
alu
e.
T
h
e
n
ex
t
s
tep
i
s
to
ca
lcu
late
t
h
e
f
i
tn
e
s
s
o
f
ea
ch
ce
ll
i
n
t
h
e
p
o
p
u
latio
n
an
d
r
an
k
th
e
m
.
I
n
th
is
ca
s
e,
t
h
e
b
es
t
ca
n
d
id
ate
w
ill b
e
ch
o
s
e
n
to
b
e
d
etec
to
r
D
.
Nex
t,
i
n
it
ialize
a
p
o
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u
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P
o
f
g
e
n
e,
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c
h
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et
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v
e
a
r
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d
o
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alu
e
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h
en
p
er
f
o
r
m
n
e
g
ati
v
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n
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h
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atc
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.
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h
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h
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o
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o
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e
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n
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an
d
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an
k
th
e
m
a
n
d
p
er
f
o
r
m
cr
o
s
s
o
v
er
an
d
m
u
tatio
n
.
L
o
o
p
if
ter
m
in
atio
n
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n
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itio
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is
n
o
t
m
et
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e
n
s
to
p
.
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h
e
p
s
u
d
o
c
o
d
e
f
o
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th
e
I
m
m
u
n
e
Ge
n
etic
A
l
g
o
r
ith
m
is
s
h
o
w
n
i
n
F
i
g
u
r
e
7
.
Pse
u
d
o
c
o
d
e
f
o
r
i
m
m
u
n
e
g
e
n
e
t
i
c
a
l
g
o
r
i
t
h
m
1:
d
←
0
;
2:
I
n
i
t
D
e
t
e
c
t
o
r
[
D
(
d
)
]
;
{
I
n
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t
i
a
l
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z
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s t
h
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d
e
t
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c
t
o
r
}
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Ev
a
l
D
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e
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t
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r
[
D
(
d
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]
;
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a
l
u
a
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h
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t
o
r
}
4:
t
←
0
;
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n
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t
P
o
p
u
l
a
t
i
o
n
[
P
(
t
)];
{
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i
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e
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h
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l
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t
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6:
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l
P
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t
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n
[
P
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t
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]
;
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l
u
a
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e
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l
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t
i
o
n
}
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M
a
t
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h
e
s[
P
(
t
)
,
D
(
d
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]
;
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t
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s b
e
t
w
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n
t
h
e
p
o
p
u
l
a
t
i
o
n
a
n
d
d
e
t
e
c
t
o
r
}
8:
w
h
i
l
e
(
n
o
t
t
e
r
mi
n
a
t
a
t
i
o
n
)
do
9:
P’
(
t
)
←
V
a
r
i
a
t
i
o
n
[
P
(
t
)
]
;
{Cre
a
t
i
o
n
o
f
n
e
w
so
l
u
t
i
o
n
s}
10:
Ev
a
l
P
o
p
u
l
a
t
i
o
n
[
P
(
t
)];
{Ev
a
l
u
a
t
e
s
t
h
e
n
e
w
so
l
u
t
i
o
n
s}
11:
P
(
t
+
1
)
←
A
p
p
l
y
G
e
n
e
t
i
c
O
p
e
r
a
t
o
r
s
[
P’
(
t
)
Q
];
{N
e
x
t
g
e
n
e
r
a
t
i
o
n
p
o
p
.
}
12:
t
←
t
+
1
;
13:
e
n
d
w
h
i
l
e
Fig
u
r
e
7
.
P
s
eu
d
o
co
d
e
f
o
r
im
m
u
n
e
g
en
e
tic
alg
o
r
it
h
m
2
.
1
0
.
M
a
t
he
m
a
t
ica
l F
un
ct
io
ns
I
n
o
r
d
er
to
co
m
p
ar
e
an
d
ev
al
u
ate
d
if
f
er
e
n
t
al
g
o
r
ith
m
s
,
r
es
ea
r
ch
er
s
h
a
v
e
b
ee
n
lo
o
k
in
g
f
o
r
v
ar
io
u
s
b
en
ch
m
ar
k
f
u
n
ctio
n
s
w
i
th
v
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u
s
p
r
o
p
er
ties
.
Fi
v
e
te
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f
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n
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I
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2
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4
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O
bje
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ate
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er
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t
alg
o
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it
h
m
s
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v
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h
m
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w
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t
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ee
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g
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ed
.
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e
s
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o
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3.
RE
SU
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S AN
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m
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s
t
f
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n
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s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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