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Octo
b
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201
8
:
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et
h
o
d
s
Naï
v
e
B
a
y
es
an
d
Neu
r
al
Net
w
o
r
k
m
et
h
o
d
s
w
ith
Aso
ciatio
n
R
u
le
s
(
7
)
.
An
o
th
er
s
t
u
d
y
co
n
d
u
c
ted
W
ij
ay
an
in
g
r
u
m
an
d
Ma
h
m
u
d
y
p
r
o
v
e
th
at
o
p
tim
iza
tio
n
f
o
r
s
ch
ed
u
li
n
g
s
h
ip
s
’
r
o
u
te
u
s
i
n
g
Ge
n
etic
Al
g
o
r
ith
m
s
ca
n
g
e
n
er
ate
n
ea
r
l
y
o
p
tim
a
l
s
o
l
u
tio
n
(
1
4
)
.
T
h
e
au
th
o
r
s
i
n
te
n
d
to
u
s
e
g
en
et
ic
alg
o
r
it
h
m
s
to
o
p
ti
m
ize
th
e
v
al
u
e
o
f
b
elie
f
in
t
h
e
m
et
h
o
d
o
f
Dem
p
s
ter
Sh
a
f
er
.
B
ased
o
n
ex
p
o
s
u
r
es
t
h
at
h
as
b
ee
n
d
es
cr
ib
ed
au
th
o
r
s
co
n
d
u
cted
a
s
t
u
d
y
tit
led
Valu
e
B
elie
f
Op
ti
m
izatio
n
I
m
p
le
m
e
n
tat
io
n
J
atr
o
p
h
a
C
u
r
ca
s
P
lan
t
Dis
ea
s
e
Dete
ctio
n
.
T
h
is
s
y
s
te
m
ca
n
id
en
ti
f
y
J
atr
o
p
h
a
C
u
r
ca
s
p
la
n
t
d
is
ea
s
e
b
ased
o
n
s
y
m
p
to
m
s
,
as
w
ell
as
p
r
o
v
id
i
n
g
b
etter
r
esu
lt
s
w
h
e
n
u
s
i
n
g
g
e
n
etic
al
g
o
r
ith
m
s
to
g
en
er
ate
v
al
u
e
b
elief
.
2.
G
E
NE
T
I
C
A
L
G
O
RI
T
H
M
I
N
DE
M
P
ST
E
R
-
SH
AF
E
R
De
m
p
s
ter
-
S
h
a
f
er
m
et
h
o
d
is
a
m
et
h
o
d
th
at
h
as
a
m
o
d
el
f
r
a
m
e
o
f
d
is
ce
r
n
m
e
n
t
w
h
ic
h
is
d
e
n
o
ted
b
y
θ
(
th
eta)
.
Fra
m
e
o
f
d
is
ce
r
n
m
e
n
t
is
t
h
e
u
n
iv
er
s
e
o
f
d
is
co
u
r
s
e
o
f
a
s
et
o
f
h
y
p
o
th
e
s
es
to
ass
o
cia
te
tr
u
s
t
ele
m
e
n
ts
θ
b
ec
au
s
e
n
o
t
all
ev
id
en
ce
d
ir
ec
tl
y
s
u
p
p
o
r
ts
ea
ch
ele
m
en
t.
Fo
r
th
at
w
e
n
ee
d
th
e
p
r
o
b
ab
ilit
y
d
en
s
i
t
y
(
m
)
,
w
h
ic
h
w
ill lo
o
k
f
o
r
th
e
lar
g
e
s
t
d
en
s
it
y
v
al
u
e
as a
r
es
u
lt o
f
t
h
e
d
ec
is
io
n
(
1
1
)
.
Gen
etic
al
g
o
r
ith
m
is
d
esi
g
n
ed
to
m
i
m
ic
o
f
t
h
e
n
at
u
r
al
s
y
s
te
m
n
ec
e
s
s
ar
y
f
o
r
ev
o
lu
t
io
n
,
in
p
ar
ticu
lar
th
e
th
eo
r
y
o
f
e
v
o
lu
tio
n
C
h
ar
l
es
Dar
w
i
n
,
th
e
s
u
r
v
iv
al
o
f
f
it
n
es
s
(
1
5
)
.
T
er
m
s
u
s
ed
in
g
en
e
tic
alg
o
r
ith
m
is
also
ad
o
p
ted
f
r
o
m
th
e
s
cie
n
ce
o
f
g
en
et
ics
s
u
c
h
as
ch
r
o
m
o
s
o
m
es,
g
en
e
s
,
cr
o
s
s
o
v
er
,
m
u
tat
io
n
,
an
d
o
th
er
s
.
I
n
ad
d
itio
n
to
th
e
ter
m
s
,
th
e
p
r
o
ce
s
s
o
f
cr
o
s
s
o
v
er
,
m
u
tatio
n
,
an
d
s
elec
tio
n
a
ls
o
ad
o
p
ted
f
r
o
m
g
e
n
etic
s
cie
n
ce
ap
p
lied
in
th
is
al
g
o
r
ith
m
(
1
6
)
.
T
h
e
w
o
r
k
i
n
g
p
r
o
ce
s
s
o
f
Gen
etic
A
l
g
o
r
ith
m
w
i
th
De
m
p
s
ter
-
S
h
af
er
i
s
a
s
f
o
llo
w
s
:
G
enet
ic
Alg
o
rit
h
m
1.
I
n
itializatio
n
p
ar
a
m
eter
.
2.
Gen
er
ate
r
an
d
o
m
f
ir
s
t
g
en
er
ati
o
n
3.
E
v
alu
a
te
th
e
f
it
n
es
s
v
al
u
e
o
f
e
ac
h
ch
r
o
m
o
s
o
m
e
i
n
th
e
p
o
p
u
la
tio
n
.
4.
Gen
er
ate
a
n
e
w
p
o
p
u
latio
n
u
s
i
n
g
t
h
e
f
o
llo
w
in
g
p
r
o
ce
s
s
:
a.
Selectio
n
: T
ak
e
t
w
o
p
ar
en
t c
h
r
o
m
o
s
o
m
e
s
f
r
o
m
t
h
e
ex
is
ti
n
g
p
o
p
u
latio
n
b.
C
r
o
s
s
o
v
er
: D
o
cr
o
s
s
o
v
er
ag
a
i
n
s
t t
w
o
p
ar
en
t c
h
r
o
m
o
s
o
m
e
s
t
o
p
r
o
d
u
ce
n
e
w
o
f
f
s
p
r
in
g
c.
Mu
tatio
n
: O
f
f
s
p
r
in
g
f
o
r
m
e
d
f
r
o
m
t
h
e
e
x
is
t
in
g
p
ar
en
t
m
u
ta
tio
n
s
5.
Ob
tain
a
n
e
w
p
o
p
u
latio
n
in
t
h
e
n
ex
t
g
en
er
at
io
n
.
6.
R
ep
ea
t th
e
p
r
o
ce
s
s
ag
ai
n
f
r
o
m
th
e
b
eg
i
n
n
i
n
g
to
f
i
n
d
th
e
d
esir
ed
n
ee
d
s
.
De
m
ps
t
er
-
S
ha
f
er
7.
T
ak
e
a
b
elief
v
alu
e
o
f
ea
c
h
cr
i
ter
io
n
s
elec
ted
.
8.
Dete
r
m
i
n
e
th
e
h
i
g
h
e
s
t b
elief
v
alu
e
o
f
ea
c
h
cr
iter
io
n
s
elec
ted
.
9.
Dete
r
m
i
n
e
th
e
p
la
u
s
ib
ili
t
y
v
al
u
e
o
f
ea
ch
cr
iter
io
n
s
elec
ted
.
10.
Do
in
g
a
s
u
b
s
et
o
f
th
e
cr
iter
ia
w
it
h
o
th
er
cr
iter
ia
g
r
ad
u
all
y
.
11.
Gettin
g
d
en
s
it
y
v
al
u
es b
ased
o
n
th
e
ca
lc
u
latio
n
s
u
b
s
et.
12.
Ma
k
e
d
ec
is
io
n
s
b
ased
o
n
th
e
h
ig
h
e
s
t d
en
s
it
y
v
al
u
e.
3.
RE
S
E
ARCH
M
E
T
H
O
D
T
h
e
d
atas
ar
e
u
s
ed
a
s
m
an
y
as
3
0
cr
iter
ia
f
o
r
s
y
m
p
to
m
s
o
f
t
h
e
d
is
ea
s
e
a
n
d
9
t
y
p
es
o
f
ill
n
es
s
.
S
y
m
p
to
m
s
ar
e
tak
en
f
r
o
m
s
ev
er
al
p
ar
ts
o
f
J
atr
o
p
h
a
as f
r
u
its
,
leav
es,
s
te
m
s
a
n
d
r
o
o
ts
.
3.
1
.
Chro
m
o
s
o
m
e
Repre
s
e
n
t
a
t
io
n
R
ep
r
esen
tat
io
n
o
f
t
h
e
c
h
r
o
m
o
s
o
m
e
w
er
e
u
s
ed
t
h
at
u
s
in
g
in
t
eg
er
r
ep
r
esen
tat
io
n
.
T
h
er
e
ar
e
2
7
0
g
en
e
s
in
o
n
e
c
h
r
o
m
o
s
o
m
e.
E
ac
h
g
e
n
e
h
a
s
a
v
al
u
e
o
f
0
-
1
0
0
r
ep
r
esen
ti
n
g
th
e
ir
r
esp
ec
tiv
e
b
elie
f
v
al
u
e
o
f
j
atr
o
p
h
a
cu
r
ca
s
p
lan
t d
is
ea
s
es.
Fi
g
u
r
e
1
s
h
o
w
s
a
n
ex
a
m
p
le
o
f
c
h
r
o
m
o
s
o
m
e
r
ep
r
esen
tatio
n.
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timiz
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emp
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ter
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eliev
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et
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Fig
u
r
e
1
.
C
h
r
o
m
o
s
o
m
e
r
ep
r
esen
tatio
n
3
.
2
.
F
i
t
nes
s
I
n
t
h
e
s
elec
tio
n
p
r
o
ce
s
s
u
s
i
n
g
t
h
e
f
it
n
e
s
s
v
al
u
e
d
er
i
v
ed
f
r
o
m
t
h
e
v
alu
e
o
f
t
h
e
ac
c
u
r
ac
y
o
f
t
h
e
ca
lcu
latio
n
b
ased
o
n
t
h
e
De
m
p
s
ter
-
S
h
a
f
er
b
elief
co
n
ta
in
ed
in
ea
ch
c
h
r
o
m
o
s
o
m
e.
T
h
er
e
ar
e
5
0
ex
a
m
p
les
o
f
ca
s
es th
a
t a
r
e
u
s
ed
f
o
r
th
e
ca
lc
u
latio
n
o
f
f
i
tn
e
s
s
v
alu
e
u
s
in
g
E
q
u
atio
n
(
1
)
.
c
a
s
e
s
of
n
u
m
b
e
r
t
o
t
a
l
t
r
u
e
is
t
h
a
t
c
a
s
e
s
of
n
u
m
b
e
r
t
h
e
f
i
t
n
e
s
s
(
1
)
3
.
3
.
Repro
du
ct
io
n
I
n
t
h
is
s
ta
g
e,
to
p
r
o
d
u
ce
o
f
f
s
p
r
in
g
.
T
h
e
m
e
th
o
d
u
s
ed
i
s
cr
o
s
s
o
v
er
a
n
d
m
u
ta
tio
n
.
T
h
i
s
p
r
o
ce
s
s
r
elie
s
o
n
th
e
cr
o
s
s
o
v
er
r
ate
an
d
m
u
t
atio
n
r
ate
ar
e
in
clu
d
ed
.
I
n
th
is
p
ap
er
,
c
r
o
s
s
o
v
er
m
eth
o
d
u
s
e
d
o
n
e
-
cu
t
p
o
in
t
an
d
m
u
tatio
n
m
et
h
o
d
u
s
ed
r
an
d
o
m
m
u
ta
tio
n
(
1
6
)
.
A
o
n
e
-
c
u
t
p
o
in
t
cr
o
s
s
o
v
er
p
r
o
ce
s
s
is
d
o
n
e
b
y
s
e
lectin
g
t
w
o
in
d
iv
id
u
als
an
d
s
elec
t
o
n
e
p
o
in
t
to
r
a
n
d
o
m
l
y
ta
k
e
t
h
e
le
f
t
f
r
o
m
t
h
e
f
ir
s
t
i
n
d
i
v
id
u
al
o
r
P1
an
d
th
e
r
ig
h
t
o
f
t
h
e
s
ec
o
n
d
in
d
i
v
id
u
al
o
r
P2
to
f
o
r
m
a
n
e
w
i
n
d
iv
id
u
al
,
as
s
h
o
w
n
in
Fi
g
u
r
e
2
.
35
34
25
26
36
47
86
13
13
57
86
45
P1
54
31
25
78
76
87
57
90
18
80
23
67
P2
35
34
25
26
36
47
57
90
18
80
23
67
C1
Fig
u
r
e
2
.
On
e
-
c
u
t p
o
in
t c
r
o
s
s
o
v
er
W
h
ile
a
r
an
d
o
m
m
u
tatio
n
p
r
o
ce
s
s
is
d
o
n
e
b
y
s
elec
ti
n
g
o
n
e
in
d
i
v
id
u
al
s
to
r
an
d
o
m
l
y
f
r
o
m
al
l
in
d
iv
id
u
als a
n
d
th
e
n
s
elec
t t
wo
p
o
in
t to
r
an
d
o
m
l
y
,
ex
c
h
an
g
e
to
f
o
r
m
a
n
e
w
i
n
d
iv
id
u
al
,
as
s
h
o
w
n
i
n
Fi
g
u
r
e
3
.
35
34
25
26
36
47
86
13
13
57
86
45
P1
35
34
25
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4752
I
n
d
o
n
esia
n
J
E
lec
E
n
g
&
C
o
m
p
Sci,
Vo
l
.
1
2
,
No
.
1
,
Octo
b
er
201
8
:
61
–
68
66
B
ased
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Fig
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9
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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2
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RE
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NC
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S
[1
]
Yu
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[2
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Ro
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W
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W
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,
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sig
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M
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Op
ti
m
iza
ti
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A
d
v
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a
ter
Re
s
[
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tern
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.
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//
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sc
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t/
A
M
R.
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3
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4
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