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,
k
-
m
e
a
n
s
c
a
n gi
v
e
g
ood s
ol
u
t
i
on
f
or
s
ol
v
e
c
lu
s
te
r
i
n
g
p
r
io
r
ity
a
r
e
a
s
.
B
as
ed
o
n
t
h
e o
b
s
er
v
at
i
o
n
s
m
ad
e o
n
t
h
e H
o
m
e I
n
d
u
s
t
r
y
o
f
t
e
m
p
e ch
i
p
s
.
H
o
m
e T
h
e i
n
d
u
s
t
r
y
h
a
s
a
w
i
d
e
m
ar
k
et
i
n
g
ar
ea an
d
l
i
m
i
t
ed
v
eh
i
cl
e
s
i
n
m
ee
t
i
n
g
cu
s
t
o
m
er
d
e
m
an
d
.
D
i
s
tr
ib
u
tio
n
i
s
s
o
m
e
ti
m
e
s
n
o
t
m
e
e
t
t
h
e t
i
m
e s
et
b
y
t
h
e cu
s
t
o
m
er
s
o
m
a
n
y
co
m
p
l
ai
n
t
s
r
ecei
v
ed
.
T
h
e ex
t
en
t
o
f
m
ar
k
et
i
n
g
ar
ea,
l
i
m
i
t
ed
v
eh
i
cl
e a
n
d
l
i
m
i
t
e
d
t
i
m
e
c
a
n b
e
o
ve
r
c
o
m
e
b
y d
o
i
ng
s
t
r
a
t
e
gi
c
p
l
a
n
ni
n
g s
u
c
h a
s
b
y
us
i
n
g r
o
ut
e
p
l
a
nni
n
g
t
hr
o
u
gh
w
hi
c
h t
he
d
is
tr
ib
u
t
i
o
n
p
r
o
ces
s
,
t
h
e
cap
a
ci
t
y
i
s
l
o
ad
ed
b
y
eac
h
v
e
h
i
cl
e
an
d
p
r
i
o
r
i
t
y
ar
ea
s
t
o
r
ecei
v
e
o
r
d
er
s
f
i
r
s
t
.
S
u
c
h
p
l
a
nni
n
g
c
a
n b
e
a
s
s
i
s
t
e
d
w
i
t
h
t
he
he
l
p
o
f
a
c
o
m
p
ut
e
r
us
i
n
g
a
m
e
t
ho
d
i
n
a
r
t
i
f
i
c
i
a
l
i
nt
e
l
l
i
ge
nc
e
l
i
ke
t
he
K
-
M
ean
s
m
et
h
o
d
[8
],
[9
]
,
[1
1
]
t
o
cat
eg
o
r
i
ze p
r
i
o
r
i
t
y
ar
eas
.
B
a
s
e
d on
t
h
e
pr
obl
e
m
s
,
t
h
e
r
es
e
ar
ch
er
p
r
o
p
o
s
ed
an
a
r
e
a
m
a
ppi
ng
m
e
t
h
od us
i
ng K
-
M
e
a
n
s
a
n
d
G
e
n
e
tic
A
l
g
o
r
ith
m
f
o
r
r
o
u
te
o
p
ti
m
iz
a
t
io
n
to
f
a
s
te
r
ti
m
e
o
f
c
o
m
p
ut
a
t
i
o
n t
ha
n c
o
n
ve
n
t
i
o
na
l
c
ur
r
e
nt
m
e
t
ho
d
i
n
ho
m
e
i
nd
u
s
t
r
y
.
T
h
e
G
e
n
e
tic
A
lg
o
r
it
h
m
c
a
n
s
o
lv
e
t
h
e
p
r
o
b
le
m
f
o
r
r
o
u
te
o
p
ti
m
iz
a
t
io
n
o
n
V
R
P
is
s
u
e
s
[
12]
W
ith
f
a
s
t
c
o
m
p
ut
i
ng
t
i
m
e
[4
]
.
T
h
e r
es
ear
ch
o
f
M
n
as
r
i
,
et
al
[
13]
t
h
e
ge
n
e
t
i
c
a
l
g
or
i
t
hm
i
s
u
s
e
d t
o opt
i
m
i
z
e
t
h
e
r
ou
t
e
on
t
he
V
R
P
T
W
p
r
ob
l
e
m
b
y
i
m
p
r
o
vi
n
g
t
he
p
r
o
c
e
s
s
o
n
t
he
c
r
o
s
s
o
ve
r
r
e
s
ul
t
i
n
g
i
n
t
he
b
e
s
t
r
o
ut
e
a
nd
m
i
n
i
m
u
m
c
o
s
t,
th
e
n
t
h
e r
es
ear
c
h
b
y
L
es
m
a
w
a
t
i
[
14]
u
s
i
ng
a
g
e
n
e
t
i
c
a
l
g
or
i
t
hm
f
or
t
h
e
di
s
t
r
i
but
i
on
of
f
r
oz
e
n
f
ood,
s
u
b
s
eq
u
en
t
r
es
ear
c
h
b
y
P
h
i
l
i
p
,
et
al
[
15]
u
s
in
g
g
e
n
e
tic
a
l
g
o
r
ith
m
f
o
r
d
is
tr
ib
u
tio
n
r
o
u
te
.
F
r
o
m
s
e
v
e
r
a
l s
t
u
d
ie
s
c
o
n
d
u
c
te
d
to
p
r
o
v
e
th
a
t
th
e
g
e
n
e
tic
a
l
g
o
r
ith
m
to
g
e
t t
h
e
o
p
ti
m
a
l r
o
u
te
w
it
h
f
a
s
t c
o
m
p
u
ti
n
g
ti
m
e
a
n
d
s
u
ita
b
le
f
or
m
u
l
t
i
obj
e
c
t
i
v
e
s
opt
i
m
i
z
a
t
i
on
pr
obl
e
m
[
16]
.
I
n
a
ddi
t
i
on
t
o t
h
e
w
i
de
s
pa
c
e
pr
obl
e
m
s
a
n
d c
o
m
pl
e
x
g
e
n
e
t
i
c
a
lg
o
r
ith
m
s
c
a
n
f
i
n
d
th
e
m
o
s
t
o
p
tim
a
l s
o
l
u
tio
n
[
17]
.
T
h
e f
o
cu
s
o
f
t
h
i
s
r
e
s
ear
ch
i
s
t
o
s
o
l
v
e t
h
e co
m
p
l
e
x
pr
obl
e
m
of
V
R
P
T
W
by
c
om
b
i
n
i
ng
k
-
m
ea
n
s
m
et
h
o
d
an
d
g
en
et
i
c al
g
o
r
i
t
h
m
o
n
t
e
m
p
e ch
ip
s
d
is
tr
ib
u
tio
n
M
al
an
g
.
K
-
m
e
a
ns
do c
l
us
t
e
r
i
n
g pr
i
or
i
t
y
a
r
e
a
s
t
h
a
t
v
i
s
i
t
i
ng b
y
v
e
h
i
c
l
e
a
n
d g
e
n
e
t
i
c
a
l
g
or
i
t
hm
do s
h
c
e
du
l
i
ng
ve
hi
c
l
e
w
hi
c
h
vi
s
i
t
i
n
g t
he
c
u
s
t
o
m
e
r
s
.
2.
RE
L
AT
E
D W
O
RK
V
ar
i
o
u
s
i
m
p
r
o
v
e
m
e
n
t
s
t
o
t
h
e
g
en
et
i
c al
g
o
r
i
t
h
m
h
av
e b
een
d
o
n
e t
o
s
o
l
v
e t
h
e V
R
P
T
W
p
r
o
b
l
em
.
A
s
m
en
t
i
o
n
ed
i
n
t
h
e r
es
ear
c
h
o
f
M
n
as
r
i
,
et
al
[
13]
T
h
e
r
e
a
r
e
m
o
d
if
ic
a
tio
n
s
to
th
e
c
h
r
o
m
o
s
o
m
e
i
n
itia
liz
a
t
io
n
p
r
o
ces
s
,
g
en
er
at
ed
b
y
S
o
l
o
m
o
n
'
s
i
n
s
er
t
i
o
n,
r
a
nd
o
m
i
nt
e
r
c
ha
n
ge
,
r
a
nd
o
m
o
r
d
e
r
i
ng.
F
ur
t
he
r
m
o
d
i
f
i
c
a
t
i
o
n
b
y
C
huny
u
a
n
d X
i
a
obo u
s
i
ng
a
c
r
os
s
-
o
r
d
er
o
p
er
at
o
r
an
d
t
h
e p
ar
t
i
al
r
o
u
t
e r
ev
er
s
al
o
p
er
at
o
r
r
o
u
t
e t
o
i
n
cr
eas
e t
h
e
c
o
nve
r
ge
nt
s
p
e
e
d
[
18]
.
R
es
ear
ch
Y
u
ce
n
u
r
a
n
d
D
e
m
i
r
el
[
19]
p
e
r
f
o
r
m
c
l
us
t
e
r
i
ng
us
i
n
g ne
a
r
e
s
t
ne
i
ghb
o
r
a
nd
g
en
et
i
c al
g
or
i
t
hm
t
o s
ol
v
e
t
h
e
pr
obl
e
m
of
V
R
P
w
i
t
h
t
h
e
m
e
r
g
e
r
of
t
h
e
s
e
tw
o
m
e
th
o
d
s
o
f
c
o
m
p
u
t
in
g
tim
e
is
us
e
d
m
o
r
e
q
ui
c
kl
y
.
C
he
ng,
e
t
a
l
[
20]
i
nc
o
r
p
o
r
a
t
i
ng k
-
m
ea
n
s
a
n
d
G
A
t
o
cr
eat
e ad
ap
t
i
v
e cl
u
s
t
er
s
ai
m
ed
at
r
ed
u
ci
n
g
t
h
e co
m
p
l
e
x
i
t
y
o
f
t
i
m
e an
d
s
p
ace.
T
h
e
r
es
u
l
t
s
s
h
o
w
t
h
at
t
h
e
m
et
h
o
d
i
s
f
ea
s
i
b
l
e an
d
ef
f
ect
i
v
e i
n
c
o
nd
uc
t
i
ng
c
l
u
s
t
e
r
a
na
l
ys
i
s
.
Z
ha
o
xi
a
a
nd
H
ui
[
21]
u
s
i
n
g
a
g
e
n
e
tic
a
l
g
o
r
it
h
m
to
d
e
te
r
m
i
n
e
th
e
i
n
itia
l c
e
n
te
r
o
f
th
e
c
lu
s
te
r
in
o
r
d
e
r
to
o
b
ta
in
m
a
x
i
m
u
m
r
e
s
u
lts
a
n
d
p
r
o
v
e
th
a
t
m
e
th
o
d
is
s
u
ita
b
le
f
o
r
u
s
e
b
o
th
in
s
m
a
l
l d
a
ta
g
r
ou
ps
a
n
d i
n
m
or
e
c
o
m
pl
e
x
da
t
a
g
r
ou
ps
.
F
u
r
t
h
er
r
e
s
ear
ch
co
n
d
u
ct
ed
b
y
K
r
i
s
hn
a
a
n
d M
ur
t
y
[
22]
co
m
b
i
n
es
a
g
en
et
i
c
al
g
o
r
i
t
h
m
an
d
k
-
m
ean
s
t
o
r
es
o
l
v
e
t
h
e
p
r
o
b
l
em
o
f
g
l
o
b
al
s
ear
ch
,
k
-
m
e
a
n
s
m
e
t
h
o
d
is
u
s
e
d
to
li
m
it
th
e
s
ear
ch
t
o
t
h
e
cr
o
s
s
o
v
er
o
p
er
at
o
r
i
n
g
e
n
et
i
c
al
g
o
r
i
t
h
m
,
p
r
o
d
u
ce
a
m
et
h
o
d
i
s
n
a
m
e
G
K
A
(
G
en
et
i
c
K
-
M
ea
n
s
A
l
g
o
r
ith
m
)
,
w
h
i
c
h i
nc
l
ud
es
t
h
e k
-
me
a
n
s
ope
r
a
t
or
on
g
e
n
e
t
i
c
a
l
g
or
i
t
hm
s
ope
r
a
t
or
.
D
i
f
f
er
en
ces
i
n
t
h
i
s
s
t
u
d
y
k
-
m
e
a
n
s
c
l
us
t
e
r
i
ng
m
e
t
ho
d
i
n
us
e
p
r
i
o
r
t
o
c
l
a
ssi
f
y
t
h
e ar
ea t
h
en
t
h
e r
es
u
l
t
o
f
t
h
e k
-
m
e
a
n
s
us
e
d
t
o
i
np
ut
o
n
g
en
et
i
c al
g
o
r
i
t
h
m
s
f
or
opt
i
m
i
z
e
d r
ou
t
e
f
ol
l
o
w
e
d.
3.
RE
S
E
ARCH
M
ETH
O
D
T
he
d
a
t
a
us
e
d
i
n
t
hi
s
s
t
ud
y
c
o
m
e
fr
o
m
a
ho
m
e
i
nd
us
t
r
y
t
e
m
p
e
ch
i
p
s
"X
Y
Z
"
M
al
an
g
a
n
d
d
i
s
t
an
c
e
d
a
t
a
m
e
a
s
ur
e
d
b
y
us
i
n
g t
he
G
o
o
g
l
e Map
s
t
ool
.
D
at
a co
n
t
ai
n
ed
i
n
at
t
ach
m
e
n
t
.
3
.1
.
K
-
M
e
a
n
s
F
or
c
l
u
s
t
e
r
i
ng
pr
oc
e
s
s
,
a
m
e
t
hod t
h
a
t
h
o
w
i
t
w
or
k
s
s
i
m
pl
e
a
n
d g
e
t
o
p
ti
m
a
l r
e
s
u
lt
s
i
s
k
-
me
a
n
s
[8
]
.
T
he
f
i
r
s
t
t
h
i
ng
don
e
i
n
t
h
e
pr
oc
e
s
s
of
c
lu
s
te
r
in
g
in
itia
liz
in
g
v
a
lu
e
s
o
f
k
is
t
h
e
n
u
m
b
e
r
o
f
c
l
u
s
t
e
r
s
,
a
nd
t
he
n
d
e
te
r
m
in
e
th
e
c
en
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m
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o
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th
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a
m
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te
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itie
s
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o
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te
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m
in
i
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g
th
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m
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la
r
it
y
o
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t
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e
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t
w
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h
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e
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te
r
p
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t o
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b
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di
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s
t
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(
D
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s
ho
w
n i
n
E
q
u
at
i
o
n
1
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A
n
o
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w
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l
l
en
t
er
t
h
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u
s
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er
t
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at
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a
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th
e
s
m
a
lle
s
t D
v
a
l
u
e
s
.
D
(
x2
,
x1
)
=
∑
=
1
(
2
−
1
)
2
,
(1
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SSN
:
25
02
-
4752
I
nd
o
ne
s
i
a
n J
E
l
e
c
E
ng
&
C
o
m
p
S
c
i
,
V
o
l.
11
, N
o
.
2
,
A
ug
us
t
2018
:
4
62
–
468
464
w
h
er
e;
p
=
di
m
e
ns
i
on
da
t
a
x1
=
pos
i
t
i
on
of
poi
n
t
1
x2
=
pos
i
t
i
on
of
poi
n
t
2
T
h
e
fl
o
w
o
f
k
-
m
e
a
n
s
c
lu
s
te
r
in
g
a
lg
o
r
it
h
m
is
a
s
f
o
llo
w
s
[9
]
:
1.
I
n
i
t
i
al
i
ze t
h
e v
al
u
e o
f
k
c
l
u
s
t
er
an
d
each
cl
u
s
t
er
cen
t
r
o
i
d
s
.
2.
D
et
er
m
i
n
e each
o
b
j
ect
i
n
cl
u
d
e
t
h
e cl
u
s
t
er
w
i
t
h
t
h
e cl
o
s
e
s
t
d
i
s
t
an
ce b
as
ed
o
n
t
h
e
v
al
u
e o
f
E
u
cl
i
d
ean
d
i
s
t
an
ce.
3.
T
o
r
ecal
cu
l
at
e t
h
e v
a
l
u
e o
f
t
h
e
cen
t
r
o
i
d
o
f
each
cl
u
s
t
er
w
i
t
h
E
qu
a
t
i
on
2.
=
1
∑
=
0
,
(2
)
V
j
i
s
t
he
va
l
ue
o
f
c
e
nt
r
o
i
d
s
f
r
o
m
c
lu
s
te
r
j
.
n
j
is
th
e
n
u
m
b
e
r
o
f
o
b
j
e
c
ts
in
c
lu
s
te
r
j
.
dat
a
p
i
s
v
ect
o
r
d
at
a
to
p
.
4.
R
ep
eat
s
t
ep
s
2
u
n
t
i
l
ce
n
t
r
o
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d
v
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n
c
h
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g
ed
o
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o
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d
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s
p
eci
f
i
c.
3
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.
G
en
et
i
c A
l
g
o
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t
h
m
T
h
e
g
e
n
e
tic
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lg
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o
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ti
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iz
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tio
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d
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D
ar
w
i
n
'
s
t
h
eo
r
y
o
f
e
vo
l
ut
i
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n
[
23]
.
T
he
p
r
o
c
e
s
s
o
f
t
he
ge
ne
t
i
c
a
l
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le
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s
s
t
o
ge
t
ne
w
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nd
i
vi
d
ua
l
s
.
1)
C
h
r
om
os
om
e
R
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pr
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s
e
n
t
a
t
i
on
T
o
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ai
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o
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e r
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at
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a s
i
m
p
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am
p
l
e i
s
g
i
v
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.
T
h
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r
e
6
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s
t
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er
s
(
P
1
1
,
P
12
,
P
21
,
P
22,
P
31,
P
3
2)
s
p
r
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d
a
c
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o
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s
3
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s
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he
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e
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l
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ve
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r
e
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2,
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c
o
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y
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o
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d
6
25 t
e
m
p
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hi
ps
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t
s
ch
r
o
m
o
s
o
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e r
e
p
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es
en
t
at
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s
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a p
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ut
a
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nt
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t
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t
h 2
s
e
gm
e
nt
s
.
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i
r
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t
s
e
gm
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t
h
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d
i
s
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an
ce
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et
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e
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o
n
d
s
e
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nt
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s
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s
ed
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o
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e
d
i
s
tr
ib
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t
io
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p
r
o
ces
s
.
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i
gur
e
1
illu
s
t
r
a
tiv
e
r
ep
r
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en
t
a
t
i
o
n
o
f
ch
r
o
m
o
s
o
m
es
.
S
eg
m
en
1
S
eg
m
en
2
P
11
P
12
P
21
P
22
P
31
P
32
V1
V2
V3
1
2
3
4
5
6
1
2
3
F
i
g
ur
e
1
.
R
ep
r
es
en
t
at
i
o
n
C
h
r
o
m
o
s
o
m
e
s
I
n
F
i
gur
e
1
,
t
he
b
l
ue
c
o
l
o
ur
i
n
d
i
c
at
es
cu
s
t
o
m
er
s
i
n
each
r
eg
i
o
n
an
d
t
h
e
y
el
l
o
w
co
l
o
u
r
c
o
nt
a
i
ni
ng
t
h
r
ee
ge
ne
s
w
hi
c
h
m
e
a
ns
t
ha
t
t
he
ge
ne
1 i
n t
he
c
o
l
um
n V
1
s
e
r
ve
c
us
t
o
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e
r
s
i
n t
he
r
e
gi
o
ns
P
11 a
n
d
P
12
,
g
e
ne
2
i
n
t
he
c
o
l
um
n V
2
s
e
r
v
e
c
us
t
o
m
e
r
s
i
n t
he
r
e
gi
o
ns
P
21
a
nd
P
22
a
s
w
e
l
l
a
s
t
he
ge
ne
3
i
n
c
o
l
u
m
n V
3
s
e
r
ve
P
31
a
nd
P
32.
T
h
e
r
e
a
r
e
50 s
t
o
r
e
s
f
or
de
s
t
i
n
a
t
i
on
s
a
n
d 3
pi
c
k
u
ps
s
o
e
a
c
h ve
hi
c
l
e
ge
t
s
16
de
s
t
i
n
a
t
i
o
ns
w
ith
a
s
s
um
e
d
6
w
o
r
ki
ng d
a
y
s
.
T
i
m
e
du
r
a
t
i
on
t
o
d
r
o
p
of
g
oo
ds
i
s
30 m
i
n
u
t
e
s
.
I
f
s
e
r
vi
ng p
a
s
s
e
s
d
e
a
d
lin
e
tim
e
,
th
e
n
cal
cu
l
at
ed
p
en
a
l
t
y
c
o
unt
.
2)
F
itn
e
s
s
C
a
lc
u
l
a
ti
o
n
Af
t
e
r
t
he
ge
ne
r
a
t
i
o
n
o
f
va
l
ue
ch
r
o
m
o
s
o
m
e t
h
en
cal
cu
l
at
i
n
g
t
he
f
i
t
ne
s
s
va
l
ue
.
T
hi
s
c
a
l
c
ul
a
t
i
o
n s
ho
w
s
t
he
a
b
i
l
i
t
y
o
f
i
n
d
i
vi
d
ua
l
s
t
o
s
ur
vi
ve
a
n
d
c
o
nt
i
nue
t
h
e
ne
xt
pr
o
c
e
s
s
[
23]
.
T
he
f
itn
e
ss
f
unc
t
i
o
n
i
s
s
h
o
w
n i
n
E
q
ua
t
i
o
n 3
.
fit
n
e
s
s
=
(
1
/ (
1
+
t
t)
)
+
(
tp
*
(
-
1
)
),
(
3)
tt
is
t
o
t
a
l tr
a
v
e
l tim
e
,
tp
is
th
e
t
o
ta
l n
u
m
b
e
r
o
f
p
e
n
a
l
ty
/ v
io
la
t
i
o
n
s
.
P
e
n
a
lty
is
c
a
lc
u
l
a
t
e
d
f
r
o
m
th
e
s
u
m
o
f
w
a
itin
g
t
i
m
e
a
nd t
he
o
v
e
r
t
i
m
e
t
o dr
o
p
p
r
od
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t
s
b
a
s
e
d
o
n t
i
m
e
w
i
nd
ow
.
3)
Cr
o
ss
o
v
e
r
P
r
o
c
e
s
s
Cr
o
s
s
o
v
e
r
p
r
o
ces
s
i
s
on
e
m
e
t
h
od
of
r
e
p
r
odu
c
t
i
on
t
o pr
o
du
c
e
n
e
w
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n
di
v
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du
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l
s
(
o
ffs
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r
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n
g
).
T
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m
e
t
ho
d
us
e
d
i
s
t
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on
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c
ut
p
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u
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l
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r
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t
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d
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m
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t
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p
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ng t
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m
b
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r
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f
pop
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t
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(
pop
s
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z
e
)
a
nd
t
he
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r
os
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e
r
r
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cr
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e
te
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m
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d
.
O
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t
h
i
s i
ss
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e
0
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cr
va
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e
5.
S
o t
h
e
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e
s
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ts
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t
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t
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s
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s
.
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ndi
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d
ua
l
s
s
e
l
ec
t
ed
ar
e
P
1
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nd
P
3.
F
i
gur
e
2
r
e
p
r
e
s
e
n
t in
d
iv
i
d
u
a
ls
s
e
le
c
te
d
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
nd
o
ne
s
i
a
n J
E
l
e
c
E
ng
&
C
o
m
p
S
c
i
I
SSN
:
2502
-
4752
K
-
M
e
ans
C
l
us
t
e
r
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ng and G
e
ne
t
i
c
A
l
gor
i
t
hm
t
o Sol
v
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V
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hi
c
l
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R
out
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…
(
A
d
y
a
n
N
u
r
A
lfiy
a
tin
)
465
S
eg
m
en
1
S
eg
m
en
2
P
11
P
12
P
21
P
22
P
31
P
32
V1
V2
V3
P1
1
2
3
4
5
6
1
2
3
P3
1
4
3
2
5
6
3
2
1
F
i
g
ur
e
2
.
C
r
os
s
ov
e
r
pr
oc
e
s
s
A
f
t
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p
a
r
en
t
s
el
e
ct
e
d
.
T
h
e n
ex
t
s
t
ep
i
s
t
o
ch
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o
s
e
r
an
d
o
m
l
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u
t
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o
f
f
t
h
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en
e f
o
r
t
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cr
o
s
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er
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s
.
F
i
gur
e
3
s
ho
w
s
t
he
c
r
o
s
s
o
ve
r
pr
oc
e
s
s
.
S
eg
m
en
1
S
eg
m
en
2
P
11
P
12
P
21
P
22
P
31
P
32
V1
V2
V3
P1
1
2
3
4
5
6
1
2
3
P3
1
4
3
2
5
6
3
2
1
C1
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25]
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26]
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[
1]
Z
. H
e
, T
. C
. E
. C
h
e
n
g
,
J
. D
o
n
g
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n
d
S
. W
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n
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s
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N
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y
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r
n
.
S
y
s
t
.
,
vol
.
4
4,
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7,
pp
.
8
22
–
83
3,
20
14
.
[
2]
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.
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M
.
C
h
in
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.
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.
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.
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l
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1
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6
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5,
20
13
.
[
3]
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20
14
.
[
4]
R
.
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a
llu
s
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m
y
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K
.
D
u
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w
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m
y
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h
a
n
a
la
k
s
m
i,
a
n
d
P
.
P
a
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th
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n
,
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p
tim
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tio
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R
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on
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l
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s
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nt
.
J
.
E
ng.
Sc
i
.
T
e
c
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.
,
vol
.
1,
no.
3,
p
p.
12
9
–
13
5,
200
9.
[
5]
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.
B
.
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H
.
R
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ana
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.
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v
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.
6,
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o.
1,
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8
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–
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,
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95
9.
[
6]
Y
. Z
h
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n
g
, “
A
H
y
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d O
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m
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s
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o.
8,
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.
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17
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7.
[
7]
Y
.
Z
h
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u
an
d
J
.
W
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ith
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,
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14
.
[
8]
J
.
M
acQ
u
een
,
“
Som
e
m
e
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h
ods
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or
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p
r
o
b
a
b
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ty
,
196
7,
pp.
2
81
–
29
7.
Evaluation Warning : The document was created with Spire.PDF for Python.
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.
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2018
:
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–
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468
[
9]
S
. K
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p
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l
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. C
h
a
w
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.
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l
l
e
l
,
D
i
s
t
r
i
b
ut
e
d a
n
d G
r
i
d C
om
put
i
ng
,
2
01
6,
pp
.
2
02
–
20
6.
[
1
0]
M
a
dh
u a
nd N
a
g
a
c
ha
ndr
i
k
a
,
“
A
N
e
w
P
a
r
a
di
g
m
f
or
D
e
ve
l
opm
e
nt
of
D
a
t
a
I
m
put
a
t
i
on A
ppr
oa
c
h f
or
M
i
s
s
i
ng
V
a
l
ue
Es
tim
a
tio
n
,
”
I
n
t.
J
.
Ele
c
tr
.
C
o
m
p
u
t.
E
n
g
.
,
v
ol
.
6,
n
o.
6,
pp.
3
22
2
–
32
2
8,
20
16
.
[
1
1]
N
.
P
.
B
a
r
bos
a
,
E
.
S
.
C
hr
i
s
t
o
,
a
nd
K
.
A
.
C
os
t
a
,
“
D
e
m
a
nd
f
or
e
c
a
s
t
i
ng
f
or
pr
oduc
t
i
on
pl
a
n
ni
ng
i
n a
f
ood c
om
pa
n
y
,
”
A
R
P
N
J
. E
n
g
.
A
p
p
l
.
S
c
i
.
,
v
ol
.
10,
no.
16
,
p
p.
71
37
–
71
41
,
2
01
5.
[
1
2]
X
.
Ha
o
a
n
d
Y.
Hu
i
l
i
,
“
T
he
G
e
ne
t
i
c
A
l
gor
i
t
hm
o
n t
he
M
ul
t
i
pl
e
-
D
e
p
o
t Ve
h
ic
le
Ro
u
ti
n
g
Pr
o
b
le
m
w
ith
Ve
h
ic
le
Shar
i
ng
,
”
S
e
c
ond
I
nt
e
r
na
t
i
o
na
l
C
onf
e
r
e
nc
e
on I
n
t
e
l
l
i
g
e
nt
C
om
put
a
t
i
on
T
e
c
hnol
og
y
a
nd A
ut
om
a
t
i
on,
20
09.
[
1
3]
S
. M
n
a
s
r
i
,
F
. A
b
b
e
s
, K
. Z
i
d
i
, a
n
d
K
. G
h
e
d
i
r
a
,
“
A M
u
lti
-
O
b
j
ect
i
ve H
yb
r
i
d
B
C
R
C
-
N
SG
A
I
I
A
l
gor
i
t
hm
t
o S
ol
v
e
t
h
e
V
R
P
T
W
,”
13t
h I
nt
.
C
onf
.
H
y
br
i
d I
nt
e
l
l
.
S
y
s
t
.
,
p
p.
60
–
6
5,
20
13.
[
1
4]
W
.
L
e
s
m
a
w
a
t
i
,
A
.
R
a
h
m
i
,
a
nd W
.
F
.
M
a
hm
ud
y
,
“
O
pt
i
m
i
z
a
t
i
on of
F
r
oz
e
n F
oo
d D
i
s
t
r
i
b
ut
i
on
us
i
ng
G
e
ne
t
i
c
A
l
g
o
r
ith
m
s
,
”
J
. E
n
v
i
r
o
n
.
E
n
g
.
Su
s
t
ai
n.
T
e
c
hn
ol
.
,
v
o
l
. 3
, n
o
.
1
,
p
p
. 5
1
–
5
8,
20
16
.
[
1
5]
A
.
P
h
ilip
,
A
.
T
a
o
f
ik
i,
a
n
d
O
.
K
e
h
in
d
e
,
“
A
G
e
n
e
tic
A
l
g
o
r
ith
m
f
o
r
S
o
lv
in
g
T
r
a
v
e
llin
g
S
a
le
s
m
a
n
P
r
o
b
le
m
,
”
I
n
t
.
J
.
A
dv
.
C
om
put
.
Sc
i
.
A
p
pl
.
,
v
o
l
. 2
, n
o
.
1
,
p
p
. 2
6
–
29
,
2
01
1.
[
1
6]
Y
.
L
an
,
“
A
H
y
b
r
i
d
F
eat
u
r
e
S
e
l
e
c
t
i
on ba
s
e
d o
n M
ut
ua
l
I
nf
or
m
a
t
i
on a
nd G
e
ne
t
i
c
A
l
g
or
i
t
hm
,
”
I
n
d
o
n
es
.
J.
E
l
ect
r
.
E
ng.
C
om
p
ut
.
Sc
i
.
,
vo
l
.
7
,
n
o.
1,
pp
.
21
4
–
22
5,
20
17
.
[
1
7]
Y
.
P
.
A
ng
g
odo,
A
.
K
.
A
r
i
y
a
ni
,
M
.
K
.
A
r
di
,
a
nd W
.
F
.
M
a
h
m
udy
,
“
O
pt
i
m
a
t
i
on of
M
ul
t
i
-
T
r
ip
V
e
h
ic
le
R
o
u
tin
g
P
r
o
b
le
m
w
ith
T
i
m
e
W
i
ndow
s
us
i
ng
G
e
ne
t
i
c
A
l
g
or
i
t
hm
,
”
J
.
E
nv
i
r
o
n.
E
ng
.
Sus
t
ai
n.
T
e
c
h
nol
.
,
v
ol
.
3,
no.
2,
p
p.
9
2
–
97,
20
17
.
[
1
8]
C
.
R
e
n a
nd X
.
W
a
ng
,
“
R
e
s
e
ar
c
h on V
R
P
op
t
i
m
i
z
i
n
g bas
e
d
on
hi
e
r
ar
c
hy
c
l
us
t
e
r
i
ng a
nd I
G
A
u
nde
r
c
om
m
on
d
is
tr
ib
u
tio
n
,
”
2
00
6 I
n
t
.
C
onf
.
C
o
m
put
.
I
nt
e
l
l
.
S
e
c
u
r.
ICCIA
S
2
0
0
6
,
v
ol
.
1,
no.
4,
p
p.
14
3
–
14
6,
20
07.
[
1
9]
G
.
N
.
Y
ü
cen
u
r
a
n
d
N
.
C
.
D
em
i
r
e
l
,
“A
n
ew
g
eo
m
et
r
i
c s
h
ap
e
-
b
a
s
e
d
g
e
n
e
tic
c
lu
s
te
r
in
g
a
lg
o
r
it
h
m
f
o
r
th
e
m
u
lti
-
de
p
ot
v
e
hi
c
l
e
r
out
i
ng
pr
o
bl
e
m
,
”
E
xp
er
t
S
ys
t
.
A
p
p
l
.
,
v
ol
.
38
,
n
o.
9,
pp
.
1
18
59
–
11
86
5,
20
11
.
[
2
0]
D
. C
h
e
n
g
, X
. D
i
n
g
, J
. Z
e
n
g
, a
n
d
N
. Y
a
n
g
, “
H
y
b
r
i
d
K
-
m
e
a
n
s
A
lg
o
r
ith
m
a
n
d
G
e
n
e
tic
A
lg
o
r
ith
m
f
o
r
C
lu
s
te
r
A
n
al
y
s
i
s
,
”
I
n
d
o
n
es
.
J.
E
l
ect
r
.
E
n
g
.
,
vo
l
.
1
2,
no
.
4
,
p
p.
29
24
–
2
93
5,
20
1
4.
[
2
1]
T
.
Z
ha
ox
i
a
a
nd Z
.
H
u
i
,
“
I
m
pr
ov
e
d K
-
m
e
a
ns
C
l
us
t
e
r
i
ng
A
l
g
or
i
t
hm
B
a
s
e
d on
G
en
et
i
c A
l
g
o
r
i
t
h
m
,
”
I
ndone
s
.
J
.
E
l
e
c
t
r
. E
n
g
.
,
vo
l
.
12
,
n
o.
3,
pp
.
1
91
7
–
1
92
3,
20
14
.
[
2
2]
K
.
K
r
i
s
h
n
a a
n
d
M
.
N
.
M
u
r
t
y
,
“G
en
et
i
c K
-
m
e
an
s
al
g
o
r
i
t
h
m
,
”
I
E
E
E
T
r
a
n
s
.
S
y
s
t
. M
a
n
, C
y
b
e
r
n
. P
a
r
t
B
C
y
b
e
r
n
.
, v
o
l
.
29,
no
.
3
,
p
p.
43
3
–
43
9,
19
99
.
[
2
3]
R
. M
a
l
h
o
t
r
a
, N
. S
i
n
g
h
,
a
nd
Y
.
S
i
ng
h,
“
G
e
ne
t
i
c
A
l
g
or
i
t
hm
s
:
C
onc
e
pt
s
,
D
e
s
i
g
n
f
or
O
pt
i
m
i
z
a
t
i
on
of
P
r
oc
e
s
s
C
o
n
tr
o
lle
r
s
,
”
C
om
put
.
I
nf
.
Sc
i
.
,
v
ol
.
4,
no.
2,
p
p.
39
–
5
4,
20
11.
[
2
4]
W
.
F
.
M
a
hm
ud
y
,
M
.
R
.
M
a
r
i
a
n,
a
nd L
.
H
.
S
.
L
uong
,
“
R
e
a
l
c
ode
d g
e
ne
t
i
c
a
l
g
or
i
t
hm
s
f
or
s
ol
v
i
ng
f
l
e
x
i
bl
e
j
ob
-
s
hop
s
c
he
dul
i
ng
pr
o
bl
e
m
–
P
a
r
t
I
I
: o
p
t
i
m
iz
a
tio
n
,
”
A
d
v.
M
a
t
er
.
R
es
.
,
v
ol
.
701
,
p
p.
36
4
–
36
9,
20
13
.
[
2
5]
Y
.
P
.
A
ng
g
odo a
nd I
.
C
ho
l
i
s
s
odi
n,
“
I
m
pr
ov
e
I
nt
e
r
va
l
O
pt
i
m
i
z
a
t
i
on of
F
L
R
us
i
ng
A
ut
o
-
S
p
e
ed
A
ccel
er
at
i
o
n
A
l
g
o
r
ith
m
,
”
T
e
l
e
c
om
uni
c
at
i
on
,
C
om
put
.
E
l
e
c
t
r
o
n.
C
on
t
ro
l
,
v
ol
.
1
6,
no.
1,
p
p.
1
–
12,
2
01
7.
[
2
6]
X
.
L
uo
,
J
.
T
u,
a
nd
L
.
H
ua
ng
,
“
O
pt
i
m
i
z
a
t
i
on of
E
x
pr
e
s
s
D
e
l
i
v
e
r
y
R
out
i
ng
P
r
ob
l
e
m
,
”
T
E
L
KO
M
NIKA
(
T
e
l
e
c
om
m
uni
c
at
i
on
C
om
p
ut
.
E
l
e
c
t
r
on.
C
ont
r
ol
)
,
v
ol
.
1
4,
no
.
3A
,
p
.
38
0,
20
16
.
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