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ra
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a
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m
a
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S
P
).
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a
l
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e
s
a
n
d
re
su
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c
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g
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s
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m
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su
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e
co
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s
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m
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co
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m
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ased
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o
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it
h
m
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C
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r
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©
2
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stit
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te o
f
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d
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.
Al
l
rig
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ts re
se
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d
.
C
o
r
r
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s
p
o
nd
ing
A
uth
o
r
:
Nag
h
a
m
A
.
A
l
-
Ma
d
i
,
Facu
lt
y
o
f
Sc
ien
ce
a
n
d
I
n
f
o
r
m
atio
n
T
ec
h
n
o
lo
g
y
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A
lza
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h
P
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iv
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iv
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m
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ail:
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g
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.
a
@
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u
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.
ed
u
.
j
o
1.
I
NT
RO
D
UCT
I
O
N
Gen
etic
alg
o
r
it
h
m
s
ar
e
u
s
ed
to
s
o
lv
e
s
e
ar
c
h
a
n
d
o
p
ti
m
iza
tio
n
p
r
o
b
le
m
s
[
1
]
.
I
n
t
h
e
ea
r
l
y
1
9
6
0
s
,
1
9
7
0
s
an
d
1
9
8
0
’
s
J
o
h
n
Ho
llan
d
an
d
h
is
s
tu
d
e
n
ts
d
ev
e
lo
p
ed
th
ese
k
i
n
d
s
o
f
al
g
o
r
ith
m
s
[
2
]
,
[
5
]
,
[
6
]
,
[
8
]
,
[
9
]
.
T
h
ese
s
ea
r
ch
tech
n
iq
u
e
s
s
o
lv
e
h
ar
d
co
m
p
lex
p
r
o
b
le
m
s
i
n
v
ar
io
u
s
d
is
cip
li
n
es,
a
n
d
th
e
y
r
el
y
m
ai
n
l
y
o
n
th
e
b
io
lo
g
ical
p
r
o
ce
s
s
o
f
e
v
o
l
u
tio
n
[
3
]
,
[
5
]
,
[
8
]
.
A
s
a
m
atter
o
f
f
ac
t,
Gen
etic
A
l
g
o
r
ith
m
s
(
G
A
s
)
ar
e
r
o
u
tin
e
s
w
h
ic
h
co
u
ld
m
a
n
a
g
e
s
e
lf
-
ad
o
p
tio
n
,
s
a
m
e
a
s
n
eu
r
al
n
et
w
o
r
k
s
.
T
h
e
y
m
i
m
ic
n
at
u
r
e
i
n
a
w
a
y
t
h
at
th
e
s
u
r
v
i
v
al
o
f
t
h
e
f
itte
s
t
is
to
p
r
o
v
id
e
n
e
w
g
e
n
er
atio
n
s
,
o
f
ap
p
r
o
x
i
m
ate
s
o
l
u
tio
n
s
[
5
]
,
[
8
]
.
A
d
d
itio
n
all
y
,
g
en
et
ic
alg
o
r
it
h
m
s
(
GAs)
w
o
r
k
w
i
th
v
ar
io
u
s
ele
m
en
t
s
“in
d
i
v
id
u
al
s
”
ea
c
h
ele
m
en
t
i
s
r
ef
er
r
ed
to
a
c
h
r
o
m
o
s
o
m
e
o
r
g
e
n
o
t
y
p
e.
A
f
it
n
es
s
s
co
r
e
is
a
s
s
i
g
n
ed
to
ea
ch
i
n
d
iv
id
u
al
r
ep
r
esen
ti
n
g
a
p
o
s
s
ib
le
s
o
l
u
tio
n
,
to
a
g
i
v
en
p
r
o
b
lem
[
1
]
-
[
3
]
,
[
8
]
,
[
9
]
.
I
n
s
o
lv
in
g
ac
ad
e
m
ic
p
r
o
b
le
m
s
Ge
n
etic
Alg
o
r
it
h
m
s
(
G
As
)
w
er
e
f
ir
s
t
u
s
ed
.
T
h
ese
p
r
o
b
lem
s
ar
e
s
u
c
h
as
t
h
e
tr
av
eli
n
g
s
ale
s
m
a
n
p
r
o
b
lem
a
n
d
t
h
e
8
Q
u
ee
n
s
p
r
o
b
lem
[
3
]
,
[
5
]
,
[
6
]
,
[
9
]
.
Yea
r
s
later
,
Gen
etic
A
l
g
o
r
ith
m
s
(
GAs)
in
cr
ea
s
ed
t
h
eir
ap
p
licatio
n
s
to
o
p
ti
m
ize
m
a
n
y
t
y
p
es
o
f
co
m
p
le
x
p
r
o
b
le
m
s
s
u
c
h
as
t
h
e
co
m
p
le
x
s
ch
ed
u
lin
g
p
r
o
b
le
m
s
,
s
p
atial
l
a
y
o
u
t,
an
d
m
an
y
o
th
er
p
r
o
b
lem
s
t
h
at
ar
e
h
ar
d
to
ef
f
icien
tl
y
s
o
lv
e
[
7
]
.
2.
T
H
E
T
RAV
E
L
I
N
G
SA
L
E
S
M
AN
P
RO
B
L
E
M
(
T
SP)
On
e
o
f
t
h
e
m
o
s
t
i
m
p
o
r
tan
t
co
m
b
i
n
ato
r
ial
p
r
o
b
lem
s
is
t
h
e
tr
av
eli
n
g
s
a
les
m
a
n
p
r
o
b
lem
(
T
SP
)
.
T
h
is
p
r
o
b
lem
is
s
i
m
p
le
to
d
ef
i
n
e
[
2
4
,
[
2
5
]
-
[
2
7
]
.
I
t
is
s
tated
as
an
NP
-
h
ar
d
o
p
ti
m
izatio
n
p
r
o
b
lem
.
I
n
t
h
i
s
p
r
o
b
lem
n
cities
m
u
s
t
b
e
v
i
s
ited
b
y
a
s
a
les
m
a
n
,
s
tar
ti
n
g
f
r
o
m
o
n
e
o
f
th
e
m
p
ass
i
n
g
th
r
o
u
g
h
ea
ch
ci
t
y
o
n
l
y
o
n
ce
,
a
n
d
r
etu
r
n
i
n
g
to
th
e
f
ir
s
t c
i
t
y
.
T
h
e
co
s
t is g
iv
e
n
f
o
r
th
e
j
o
u
r
n
e
y
.
F
in
all
y
,
th
e
m
i
n
i
m
u
m
co
s
t is r
e
q
u
ir
ed
to
s
o
lv
e
th
is
p
r
o
b
lem
[
2
3
]
,
[
2
8
]
-
[
3
0
]
.
T
h
e
T
r
av
elin
g
Sales
m
a
n
P
r
o
b
lem
(
T
S
P
)
is
d
eter
m
i
n
ed
as
f
o
llo
w
s
:
Gi
v
e
n
N
cities,
Evaluation Warning : The document was created with Spire.PDF for Python.
IJ
-
AI
I
SS
N:
2252
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8938
A
g
e
C
o
n
s
tr
a
in
ts
E
ffective
n
e
s
s
o
n
th
e
Hu
ma
n
C
o
mmu
n
ity
B
a
s
ed
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(
N
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g
h
a
m
A
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l
-
Ma
d
i
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k
n
o
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n
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o
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es,
a
d
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n
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atr
i
x
w
h
er
e,
D
=
[
d
ij
]
,
co
n
s
is
ts
o
f
t
h
e
d
is
tan
ce
b
et
w
ee
n
ci
t
y
i
an
d
cit
y
j
[
2
4
]
,
[
2
8
]
,
[
2
9
]
,
[
3
0
]
.
I
n
an
atte
m
p
t
to
f
i
n
d
in
g
n
ea
r
o
p
tim
al
s
o
lu
tio
n
s
f
o
r
NP
-
h
ar
d
p
r
o
b
lem
s
;
t
h
e
T
r
av
elin
g
Sales
m
an
P
r
o
b
lem
(
T
SP
)
is
co
n
s
id
er
ed
a
s
tan
d
ar
d
b
en
ch
m
ar
k
p
r
o
b
lem
f
o
r
co
m
b
i
n
ato
r
ial
m
et
h
o
d
s
[
2
9
]
.
I
t
p
r
o
v
id
es
a
s
tan
d
ar
d
o
p
ti
m
izatio
n
te
s
t b
ed
,
to
f
in
d
n
ea
r
o
p
ti
m
u
m
s
o
l
u
tio
n
s
to
NP
-
h
ar
d
p
r
o
b
lem
s
[
1
]
,
[
31
]
,
[
36
]
,
[
3
8
]
.
T
h
e
tr
av
elin
g
s
ales
m
a
n
p
r
o
b
le
m
(
T
SP
)
is
ca
lled
Sy
m
m
etr
ic
T
SP
(
Stan
d
ar
d
)
,
if
th
e
co
s
t
b
et
w
ee
n
an
y
t
w
o
ci
ties
ar
e
eq
u
al
in
b
o
th
d
ir
ec
tio
n
s
,
t
h
i
s
m
ea
n
s
,
t
h
e
d
is
tan
ce
f
r
o
m
cit
y
i
to
cit
y
j
is
t
h
e
s
a
m
e
a
s
t
h
e
d
is
ta
n
ce
f
r
o
m
cit
y
j
to
cit
y
i.
Ot
h
er
w
i
s
e,
th
e
T
r
av
eli
n
g
Sales
m
a
n
P
r
o
b
lem
(
T
SP
)
is
to
b
e
k
n
o
w
n
as
an
As
y
m
m
etr
ic
T
SP
,
w
h
ic
h
m
ea
n
s
th
a
t
th
e
d
is
tan
ce
b
et
w
ee
n
cit
y
i
to
cit
y
j
,
d
if
f
er
s
th
a
n
t
h
e
d
is
tan
ce
f
r
o
m
cit
y
j
to
city
i
[
2
4
]
,
[
2
7
]
,
[
3
1
]
.
T
o
s
o
lv
e
th
e
tr
av
eli
n
g
s
ales
m
a
n
p
r
o
b
lem
(
T
SP
)
th
er
e
ar
e
t
w
o
alter
n
ativ
e
ap
p
r
o
ac
h
es.
Fir
s
t
,
is
to
f
i
n
d
its
s
o
l
u
tio
n
a
n
d
tr
y
p
r
o
v
in
g
it
s
o
p
tim
al
it
y
,
w
h
ic
h
ta
k
es
a
lo
n
g
p
er
io
d
o
f
ti
m
e.
|
Seco
n
d
,
to
f
in
d
an
ap
p
r
o
x
i
m
ate
s
o
lu
tio
n
in
a
s
h
o
r
t p
er
io
d
o
f
tim
e
[
2
7
]
.
A
p
p
l
y
in
g
t
h
e
T
r
av
elin
g
Sa
les
m
an
P
r
o
b
lem
u
s
i
n
g
m
et
h
o
d
s
f
r
o
m
m
an
y
s
p
ec
if
ic
ar
ea
s
m
o
s
tl
y
b
ased
o
n
s
ea
r
ch
h
eu
r
i
s
tic
m
eth
o
d
s
s
u
c
h
as
lo
ca
l
s
ea
r
ch
[
3
4
]
,
[
3
6
]
,
s
i
m
u
lated
a
n
n
ea
lin
g
[
3
2
]
,
[
3
7
]
,
tab
u
s
ea
r
c
h
[
3
3
]
,
[
3
7
]
,
n
eu
r
al
n
et
w
o
r
k
s
[
3
2
]
,
[
3
5
]
,
an
d
g
en
et
ic
al
g
o
r
ith
m
s
[
3
4
]
,
[
3
8
]
.
A
ctu
al
l
y
,
th
er
e
ar
e
w
id
e
ap
p
licatio
n
s
o
f
t
h
e
T
SP
,
s
u
ch
as,
tr
a
f
f
ic
r
o
u
te,
co
m
p
u
ter
ca
b
li
n
g
,
r
o
b
o
t c
o
n
tr
o
l a
n
d
m
a
n
y
o
t
h
er
s
[
2
5
]
,
[
2
7
]
.
3.
M
AT
E
RIAL
S AN
D
M
E
T
H
O
DS
I
n
th
e
s
elec
tio
n
p
ar
t
i
n
t
h
e
Si
m
p
le
S
tan
d
ar
d
Gen
et
ic
A
l
g
o
r
ith
m
(
SG
A
)
t
h
er
e
ar
e
n
o
co
n
s
tr
ain
ts
.
T
h
e
Si
m
p
le
Sta
n
d
ar
d
Gen
eti
c
A
l
g
o
r
it
h
m
(
SG
A
)
w
o
r
k
s
r
an
d
o
m
l
y
[
1
1
]
.
Du
e
to
th
i
s
r
an
d
o
m
n
e
s
s
,
m
a
n
y
r
esear
ch
es
ar
e
w
o
r
k
i
n
g
to
tack
le
th
i
s
p
r
o
b
lem
b
y
d
esi
g
n
i
n
g
s
tr
u
c
tu
r
ed
p
o
p
u
latio
n
an
d
p
u
ttin
g
s
o
m
e
co
n
s
tr
ain
ts
to
co
n
tr
o
l
th
e
in
d
i
v
id
u
al
’
s
i
n
ter
ac
tio
n
[
1
1
]
.
I
n
t
h
e
last
f
e
w
y
ea
r
s
m
a
n
y
t
y
p
es
an
d
m
o
d
els
o
f
G
A
s
ap
p
ea
r
ed
s
u
ch
as
t
h
e
C
el
lu
lar
G
A
[
1
1
]
,
I
s
lan
d
G
A
[
1
2
]
,
P
atch
w
o
r
k
G
A
[
1
3
]
,
[
1
4
]
,
T
er
r
a
in
-
B
ased
G
A
[
1
5
]
,
an
d
r
elig
io
n
-
B
ased
G
A
[
1
6
]
.
3
.
1
.
Cellula
r
G
As (
CG
A)
A
d
i
f
f
u
s
io
n
m
o
d
el
o
f
a
t
w
o
-
d
im
en
s
io
n
a
l
g
r
id
i
n
w
h
ich
ea
c
h
in
d
iv
id
u
al
i
n
ter
ac
ts
w
i
th
a
n
o
t
h
er
b
y
it
s
d
ir
ec
t
n
eig
h
b
o
u
r
[
1
7
]
,
[
1
1
]
.
T
h
e
Ge
n
etic
Alg
o
r
it
h
m
is
d
esi
g
n
ed
as
a
p
r
o
b
ab
ilis
tic
ce
llu
lar
au
to
m
atio
n
in
th
is
t
y
p
e
o
f
G
A
s
.
T
h
ese
in
d
i
v
id
u
als
w
ill
b
e
d
i
s
tr
ib
u
ted
o
n
a
g
r
ap
h
w
h
ic
h
i
s
co
n
n
ec
ted
to
g
et
h
er
,
h
a
v
i
n
g
a
n
eig
h
b
o
r
h
o
o
d
o
f
s
o
m
e
g
e
n
etic
o
p
er
ato
r
to
w
o
r
k
w
i
th
.
T
h
is
t
y
p
e
o
f
G
A
s
i
s
d
esig
n
ed
as
a
p
r
o
b
ab
ilis
tic
ce
llu
lar
.
A
s
el
f
-
o
r
g
an
izin
g
s
ch
ed
u
le
i
s
ad
d
ed
to
r
ep
r
o
d
u
ce
an
o
p
er
ato
r
[
1
8
]
.
T
h
e
in
d
iv
id
u
al
w
h
ic
h
c
an
i
n
ter
ac
t
w
i
th
it
s
i
m
m
ed
iate
n
ei
g
h
b
o
r
s
ca
n
o
n
l
y
b
e
h
eld
in
th
e
ce
ll.
3
.
2
.
T
er
ra
in
-
ba
s
ed
G
A
(
T
B
G
A)
I
n
a
co
m
p
ar
is
o
n
b
et
w
ee
n
th
e
T
er
r
ain
-
b
ased
G
A
(
T
B
GA
)
an
d
th
e
C
ell
u
lar
G
A
(
C
G
A
)
,
th
e
f
ir
s
t
s
h
o
ws
a
m
o
r
e
s
el
f
-
t
u
n
i
n
g
m
o
d
el
in
w
h
ic
h
m
a
n
y
co
m
b
i
n
atio
n
p
ar
a
m
eter
v
al
u
e
s
w
ill
b
e
lo
ca
ted
in
d
if
f
er
e
n
t
p
h
y
s
ica
l
lo
ca
tio
n
s
an
d
b
etter
p
er
f
o
r
m
a
n
ce
w
it
h
less
p
ar
a
m
e
ter
tu
n
in
g
th
a
n
t
h
e
s
ec
o
n
d
[
1
5
]
.
A
t
ev
er
y
g
e
n
er
atio
n
ea
ch
in
d
iv
id
u
al
s
h
o
u
ld
b
e
p
r
o
ce
s
s
e
d
,
an
d
t
h
e
m
ati
n
g
w
i
ll
b
e
s
ele
cted
f
r
o
m
t
h
e
b
est
o
f
f
o
u
r
s
tr
i
n
g
s
,
lo
ca
ted
ab
o
v
e,
b
elo
w
,
lef
t,
r
i
g
h
t.
3
.
3
.
P
a
t
chwo
rk
M
o
del
Kr
in
k
[
1
3
]
in
tr
o
d
u
ce
d
th
is
m
o
d
el
w
h
ic
h
co
n
s
is
t
s
o
f
s
e
v
er
al
id
ea
s
m
er
g
ed
to
g
et
h
er
f
r
o
m
ce
ll
u
lar
ev
o
lu
tio
n
ar
y
alg
o
r
it
h
m
s
,
is
la
n
d
m
o
d
el
s
,
an
d
tr
ad
itio
n
al
e
v
o
lu
tio
n
ar
y
alg
o
r
it
h
m
s
.
Her
e
th
e
g
r
id
is
a
t
w
o
d
i
m
en
s
io
n
al
g
r
id
o
f
f
ield
s
,
e
ac
h
f
ield
ca
n
h
av
e
a
f
i
x
ed
n
u
m
b
er
o
f
i
n
d
iv
id
u
als.
T
h
e
p
atch
w
o
r
k
m
o
d
el
i
s
co
n
s
id
er
ed
a
s
el
f
-
o
r
g
an
ized
,
s
p
atial
p
o
p
u
latio
n
s
tr
u
c
tu
r
e
[
1
9
]
.
I
n
a
GA
p
o
p
u
latio
n
,
i
n
o
r
d
er
to
allo
w
s
el
f
-
ad
ap
tatio
n
,
p
atc
h
w
o
r
k
m
o
d
el
is
u
s
ed
as
a
b
ase.
I
t
c
o
n
tain
s
a
g
r
id
w
o
r
ld
an
d
s
o
m
e
in
ter
esti
n
g
ag
e
n
ts
.
I
n
m
o
d
ell
in
g
b
io
lo
g
ical
s
y
s
te
m
s
t
h
e
p
atch
w
o
r
k
m
o
d
el
is
co
n
s
id
er
ed
as a
g
en
er
al
ap
p
r
o
ac
h
.
3
.
4
.
I
s
la
nd
M
o
dels
I
s
lan
d
m
o
d
els
ar
e
co
n
s
id
er
ed
a
f
a
m
il
y
o
f
m
o
r
e
ad
v
an
ce
d
m
o
d
els
o
f
ev
o
lu
tio
n
ar
y
al
g
o
r
ith
m
s
(
E
A
s
)
[
2
0
]
.
T
h
ese
m
o
d
els
w
h
er
e
d
ev
elo
p
ed
in
o
r
d
er
to
s
o
lv
e
m
o
r
e
co
m
p
le
x
p
r
o
b
le
m
s
w
h
ic
h
ar
e
i
n
cr
ea
s
i
n
g
r
ap
id
l
y
.
Her
e
th
e
in
d
iv
id
u
als
ar
e
d
iv
id
ed
in
to
s
ec
tio
n
s
.
W
e
ca
ll
ea
ch
s
ec
tio
n
a
s
u
b
p
o
p
u
latio
n
w
h
ic
h
is
r
ef
er
r
ed
to
as
an
is
lan
d
.
T
h
ese
is
lan
d
m
o
d
els
a
r
e
ab
le
t
o
s
o
lv
e
p
r
o
b
lem
s
in
a
b
etter
p
er
f
o
r
m
a
n
ce
th
a
n
s
ta
n
d
ar
d
m
o
d
els
[
1
8
]
.
T
h
er
e
is
a
s
p
ec
if
ic
r
elatio
n
b
et
w
ee
n
is
la
n
d
s
t
h
r
o
u
g
h
s
o
m
e
ex
ch
a
n
g
e
o
f
s
o
m
e
in
d
i
v
id
u
a
ls
b
et
w
ee
n
is
la
n
d
s
.
T
h
is
p
r
o
ce
s
s
is
ca
lled
m
i
g
r
atio
n
;
th
is
is
w
h
at
i
s
lan
d
m
o
d
el
s
ar
e
f
a
m
o
u
s
o
f
,
an
d
w
it
h
o
u
t
th
e
s
e
m
ig
r
at
io
n
s
,
ea
c
h
is
lan
d
is
co
n
s
id
er
ed
as a
s
et
o
f
s
ep
ar
ate
r
u
n
.
T
h
er
ef
o
r
e
m
ig
r
a
tio
n
is
v
er
y
i
m
p
o
r
ta
n
t
[
2
0
]
,
[
2
2
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8938
IJ
-
AI
Vo
l.
7
,
No
.
2
,
J
u
n
e
20
1
8
:
7
8
–
8
2
80
3
.
5
.
Relig
io
n
-
B
a
s
ed
E
A
M
o
del (
RB
E
A)
T
h
is
m
o
d
el
h
as
a
r
elig
io
u
s
co
n
ce
p
t
in
tr
o
d
u
ce
d
b
y
R
e
n
e
T
h
o
m
s
en
et
al.
T
h
e
R
elig
io
n
-
B
ased
E
A
Mo
d
el
(
R
B
E
A
)
attr
ac
t
s
n
e
w
b
eliev
er
s
to
a
r
eli
g
io
n
w
h
ic
h
p
u
ts
m
o
r
e
co
n
tr
o
l
t
h
a
n
o
t
h
er
m
o
d
el
s
s
u
c
h
as
ce
llu
lar
E
A
an
d
t
h
e
p
atch
w
o
r
k
m
o
d
els
[
1
6
]
.
3
.
6
.
H
um
a
n Co
mm
u
n
it
y
B
a
s
ed
G
e
net
ic
Alg
o
rit
h
m
(
H
CB
G
A)
T
h
e
p
r
o
ce
s
s
o
f
m
ati
n
g
i
n
a
h
u
m
an
co
m
m
u
n
i
t
y
i
s
n
o
r
m
all
y
c
o
n
d
u
cted
th
r
o
u
g
h
m
ar
r
iag
e.
M
ar
r
iag
es i
n
m
o
s
t
co
m
m
u
n
ities
allo
w
an
el
ig
ib
le
m
ale
a
n
d
f
e
m
ale
to
f
o
r
m
a
f
a
m
il
y
.
As
s
u
ch
,
H
C
B
GA
h
as
m
ar
r
ia
g
e
as
th
e
n
e
w
e
n
h
an
ce
m
e
n
t
b
esid
es
g
e
n
d
er
s
e
g
r
eg
atio
n
a
n
d
b
alan
ce
d
p
o
p
u
latio
n
f
r
o
m
t
h
e
p
r
ev
i
o
u
s
e
n
h
a
n
ce
m
e
n
ts
.
So
cial
co
n
s
tr
ain
t
s
ap
p
lied
to
th
is
n
e
w
ap
p
r
o
ac
h
w
er
e
af
f
e
ctiv
e.
Fig
u
r
e
1
s
h
o
w
s
th
e
m
o
d
el
o
f
t
h
e
h
u
m
a
n
co
m
m
u
n
it
y
b
a
s
ed
Gen
etic
A
l
g
o
r
ith
m
(
H
C
B
G
A
)
[
6
]
.
Fig
u
r
e
1
.
T
h
e
T
o
tal
A
v
er
ag
e
o
f
1
0
r
u
n
s
ea
c
h
b
et
w
ee
n
SG
A
&
i
m
p
r
o
v
ed
H
C
B
G
A
w
it
h
Sev
e
n
Fig
u
r
e
2
.
T
h
e
T
o
tal
A
v
er
ag
e
o
f
B
est f
i
ts
o
f
1
0
r
u
n
s
ea
ch
b
et
w
ee
n
SG
A
&
I
m
p
r
o
v
ed
HC
B
GA
w
i
th
s
ev
e
n
cities
3
.
7
.
Chro
m
o
s
o
m
e
Repre
s
e
n
t
a
t
io
n in t
he
I
m
pro
v
ed
H
CB
G
A
A
cc
o
r
d
in
g
to
t
h
e
H
u
m
an
C
o
m
m
u
n
it
y
B
ased
Ge
n
etic
Alg
o
r
ith
m
(
HC
B
G
A
)
[
6
]
,
w
h
ic
h
i
s
b
ased
o
n
n
atu
r
e
an
d
s
o
cial
s
e
lectio
n
,
a
u
th
o
r
s
i
m
p
r
o
v
e
t
h
e
H
C
B
G
A
.
T
h
is
is
d
o
n
e
b
y
u
s
in
g
th
e
u
n
d
er
-
ag
e
as
co
n
s
tr
ai
n
t
s
o
n
p
r
o
p
o
s
in
g
m
ar
r
iag
e
s
b
et
w
ee
n
m
a
les
a
n
d
f
e
m
ales
as
in
th
e
r
ea
l
h
u
m
a
n
co
m
m
u
n
i
t
y
li
f
e.
As
s
u
ch
,
a
n
attr
ib
u
te
is
g
i
v
e
n
to
ea
ch
in
d
iv
id
u
al
i
n
th
e
p
o
p
u
latio
n
s
p
ec
i
f
y
in
g
its
s
e
x
w
h
et
h
er
m
ale
o
r
f
e
m
ale.
I
n
ad
d
itio
n
,
b
ein
g
i
n
t
h
e
s
a
m
e
s
o
ciet
y
-
a
s
th
e
p
o
p
u
latio
n
i
s
d
iv
id
ed
in
to
s
u
b
g
r
o
u
p
s
o
r
is
lan
d
s
-
i
s
a
d
ep
en
d
ab
le
co
n
s
tr
ai
n
t
f
o
r
r
ec
o
m
b
i
n
atio
n
.
T
h
e
p
r
o
b
lem
o
f
a
g
e
is
co
n
s
id
er
ed
also
b
y
a
d
d
in
g
an
at
tr
ib
u
te
f
o
r
th
e
ag
e.
T
h
e
ag
e
attr
ib
u
te
tak
es
t
h
r
ee
v
al
u
es
:
y
o
u
th
,
p
ar
en
t,
an
d
g
r
a
n
d
p
ar
en
t.
I
n
ad
d
itio
n
,
a
n
e
w
r
estrictio
n
o
f
a
g
e
is
a
d
d
ed
,
as
s
u
ch
an
y
in
d
iv
id
u
al
less
t
h
an
1
5
is
n
o
t
elig
ib
le
to
b
e
s
elec
ted
.
Th
is
c
h
r
o
m
o
s
o
m
e
r
ep
r
esen
ta
tio
n
(
th
e
p
r
esen
ce
o
f
f
at
h
er
an
d
m
o
th
er
p
o
in
ter
s
)
w
ill
k
e
ep
all
f
a
m
il
y
r
elatio
n
s
w
h
ich
d
iv
id
es
th
e
s
u
b
g
r
o
u
p
s
in
to
a
Dir
ec
ted
A
c
y
clic
Gr
ap
h
(
DA
G)
.
A
ll
t
h
e
s
ta
n
d
ar
d
o
p
e
r
atio
n
s
in
t
h
e
G
A
w
ill
b
e
ch
an
g
ed
i
n
o
r
d
er
to
a
d
d
r
estrictio
n
s
o
n
ea
ch
o
p
er
atio
n
in
clu
d
i
n
g
:
So
c
ial
co
n
s
tr
ai
n
ts
s
u
c
h
as
t
h
e
Ma
le/
Fe
m
ale
'
o
p
er
ato
r
'
an
d
u
n
d
er
-
a
g
e
r
estrictio
n
s
w
i
ll
b
e
ad
d
ed
in
th
e
s
elec
tio
n
p
ar
t
w
h
ich
w
il
l
r
estrict
ch
o
o
s
in
g
t
wo
d
if
f
er
en
t
co
u
p
les.
I
n
ad
d
itio
n
th
e
B
ir
th
o
p
er
ato
r
w
h
ic
h
is
g
en
er
ati
n
g
a
n
e
w
p
o
p
u
latio
n
,
a
n
d
th
e
Dea
t
h
o
p
er
ato
r
w
h
ich
w
i
ll d
is
ca
r
d
th
e
w
o
r
s
e
in
d
iv
id
u
als.
3
.
8
.
T
he
(
H
CB
G
A)
M
et
ho
d
I
n
itiall
y
,
t
h
e
f
ir
s
t
i
n
d
iv
id
u
al
is
s
elec
ted
r
a
n
d
o
m
l
y
f
r
o
m
th
e
p
o
p
u
latio
n
ac
co
r
d
in
g
to
its
g
r
o
w
n
u
p
ag
e
-
th
is
w
ill
b
e
th
e
f
ir
s
t
p
ar
en
t.
I
n
ad
d
itio
n
to
th
e
f
ir
s
t
p
ar
en
t
’
s
t
y
p
e
(
w
h
et
h
er
a
m
a
le
o
r
a
f
e
m
ale)
,
th
e
n
o
r
m
a
l
ag
e
o
f
m
ar
r
iag
e
s
h
o
u
ld
b
e
s
atis
f
ie
d
,
ac
co
r
d
in
g
l
y
,
th
e
s
ec
o
n
d
p
ar
en
t
w
ill
b
e
ch
o
s
en
s
u
c
h
t
h
at
it
is
th
e
o
p
p
o
s
ite
t
y
p
e
o
f
th
e
f
ir
s
t
p
ar
en
t
in
ad
d
itio
n
to
its
r
estricte
d
g
r
o
w
n
u
p
ag
e.
T
h
is
p
r
o
ce
s
s
is
r
ep
ea
te
d
f
o
r
a
n
u
m
b
er
o
f
in
d
iv
id
u
als
cr
ea
ti
n
g
t
h
e
i
n
itial
p
o
p
u
latio
n
.
Ne
x
t
co
m
e
s
t
h
e
s
tag
es
o
f
s
elec
t
io
n
a
n
d
cr
o
s
s
o
v
er
,
b
r
in
g
i
n
g
u
p
t
w
o
n
e
w
ch
i
ld
r
en
o
r
o
f
f
s
p
r
i
n
g
’
s
.
R
ep
ea
tin
g
t
h
is
f
o
r
a
n
u
m
b
er
o
f
co
u
p
les
a
s
ec
o
n
d
p
o
p
u
latio
n
w
ill
b
e
g
e
n
er
ated
.
Ag
ai
n
,
t
h
e
p
r
ev
io
u
s
p
r
o
ce
s
s
is
r
ep
ea
ted
u
n
t
il
t
h
e
m
a
x
i
m
u
m
n
u
m
b
er
o
f
g
e
n
er
atio
n
s
i
s
r
ea
c
h
ed
.
(
T
h
e
n
ex
t
m
ai
n
i
m
p
o
r
tan
t t
h
i
n
g
i
s
th
at
t
h
e
t
w
o
in
d
iv
id
u
als
m
u
s
t
n
o
t sh
ar
e
t
h
e
s
a
m
e
p
ar
en
t
s
)
.
4.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
I
n
t
h
is
r
esear
ch
w
e
h
a
v
e
u
s
ed
th
e
T
r
av
elin
g
s
ales
m
a
n
P
r
o
b
le
m
(
T
SP
)
to
test
th
e
i
m
p
r
o
v
ed
Hu
m
an
C
o
m
m
u
n
i
t
y
B
ased
Ge
n
etic
Alg
o
r
ith
m
(
H
C
B
G
A
)
o
f
[
6
]
.
W
e
also
u
s
ed
i
t
as
a
test
o
n
th
e
S
i
m
p
le
Sta
n
d
ar
d
Gen
etic
A
l
g
o
r
ith
m
(
SG
A
)
i
n
o
r
d
er
to
c
o
m
p
ar
e
b
et
w
ee
n
b
o
th
alg
o
r
i
th
m
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
IJ
-
AI
I
SS
N:
2252
-
8938
A
g
e
C
o
n
s
tr
a
in
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E
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o
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81
A
p
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ize
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w
it
h
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cities
a
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d
ar
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d
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[
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]
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A
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el.
T
h
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p
lace
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th
e
ch
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o
m
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s
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m
e
at
w
h
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w
it
h
p
r
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b
ab
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h
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cr
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f
th
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cr
o
s
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er
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o
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o
cc
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r
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e
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th
e
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its
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p
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e
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d
o
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teg
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f
th
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t
w
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m
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m
e
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w
ap
p
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d
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h
e
m
u
tatio
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t
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a
r
an
d
o
m
c
h
a
n
g
e
to
a
g
en
e
v
al
u
e
[
3
9
]
,
[
4
0
]
.
A
f
ter
s
ev
er
al
e
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p
er
i
m
e
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ts
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tatio
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ates,
t
h
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o
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itab
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0
4
.
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h
e
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tio
n
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et
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ed
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h
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m
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er
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is
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h
e
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m
p
le
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en
ta
tio
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ar
t
w
a
s
p
r
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g
r
a
m
m
ed
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n
C
#
(
C
S
h
ar
p
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L
an
g
u
a
g
e
Ver
s
io
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5
.
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o
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m
4
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l
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g
t
h
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n
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lem
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T
SP
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n
b
o
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th
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S
i
m
p
le
Stan
d
ar
d
Gen
e
tic
A
l
g
o
r
ith
m
(
SGA
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a
n
d
o
n
t
h
e
i
m
p
r
o
v
ed
Hu
m
a
n
C
o
m
m
u
n
it
y
B
ased
Ge
n
etic
Alg
o
r
it
h
m
(
HC
B
G
A
)
[
6
]
w
e
ca
n
co
m
p
ar
e
t
h
e
p
er
f
o
r
m
a
n
ce
o
f
b
o
t
h
al
g
o
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ith
m
s
.
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h
e
f
o
llo
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i
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m
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s
h
o
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t
h
e
c
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tr
ain
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u
t
o
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th
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e
w
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u
m
a
n
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o
m
m
u
n
i
t
y
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ased
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n
etic
A
l
g
o
r
ith
m
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A
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g
av
e
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etter
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er
f
o
r
m
a
n
ce
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an
t
h
e
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m
p
le
Sta
n
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ar
d
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l
g
o
r
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th
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h
ich
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ep
en
d
s
m
ai
n
l
y
o
n
it
s
r
an
d
o
m
n
es
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i
n
f
i
n
d
in
g
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h
e
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est
f
it
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t
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n
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t
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o
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t
h
at
in
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h
e
i
m
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r
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ed
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m
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C
o
m
m
u
n
i
t
y
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ased
Gen
etic
Alg
o
r
ith
m
(
HC
B
G
A
)
th
e
a
v
er
ag
e
co
n
v
er
g
e
to
w
ar
d
t
h
e
o
p
ti
m
al
s
o
lu
t
io
n
b
etter
t
h
a
n
t
h
e
Si
m
p
le
Sta
n
d
ar
d
Gen
eti
c
A
l
g
o
r
ith
m
(
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G
A
)
,
an
d
th
e
b
es
t
f
it
v
alu
e
s
i
n
t
h
e
i
m
p
r
o
v
ed
Hu
m
a
n
C
o
m
m
u
n
it
y
B
ased
Gen
etic
A
l
g
o
r
it
h
m
(
H
C
B
G
A
)
also
s
h
o
w
b
etter
f
in
d
i
n
g
s
o
f
b
est
f
it v
a
lu
es in
a
co
m
p
ar
is
o
n
to
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e
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asic
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m
p
le
Sta
n
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ar
d
Gen
etic.
I
n
th
e
f
o
llo
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in
g
F
i
g
u
r
es
w
e
ca
n
s
ee
t
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if
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tern
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ti
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o
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e
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e
ti
c
A
l
g
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rit
h
m
s
,
F
o
rre
st S
.
(e
d
.
).
M
o
r
g
a
n
Ka
u
fm
a
n
n
6
5
8
.
[1
8
]
A
lb
a
,
E.
,
G
iac
o
b
in
i,
M
.
,
T
o
m
a
ss
in
i,
M
.
,
2
0
0
2
,
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o
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rin
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S
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ro
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c
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Ce
ll
u
lar
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e
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ti
c
A
l
g
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rit
h
m
s.
In
J
.
J
.
M
e
re
lo
e
t
a
l.
,
e
d
it
o
r,
Pa
r
a
ll
e
l
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b
lem
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o
lvi
n
g
fro
m
N
a
tu
re
–
PP
S
N
V1
1
,
S
p
rin
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r
-
Ver
la
g
,
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id
e
lb
e
rg
.
2
4
3
9
:
6
0
1
-
6
1
0
.
[1
9
]
Urs
e
m
,
R.
K.,
1
9
9
9
,
M
u
lt
i
n
a
t
io
n
a
l
e
v
o
lu
ti
o
n
a
ry
a
l
g
o
rit
h
ms
.
In
P
ro
c
e
e
d
i
n
g
s
o
f
th
e
Co
n
g
re
ss
o
f
Ev
o
lu
ti
o
n
a
ry
Co
m
p
u
tatio
n
,
3
:
1
6
3
3
-
1
6
4
0
.
[2
0
]
Zb
ig
n
iew
S
.
,
De
Jo
n
g
,
K.,
2
0
0
5
,
T
h
e
i
n
fl
u
e
n
c
e
o
f
mig
ra
t
io
n
size
s
a
n
d
i
n
ter
v
a
ls
o
n
isl
a
n
d
m
o
d
e
ls
.
I
n
P
r
o
c
e
e
d
in
g
s
o
f
G
e
n
e
ti
c
a
n
d
Ev
o
lu
ti
o
n
a
ry
Co
m
p
u
tatio
n
C
o
n
f
e
re
n
c
e
–
G
ECCO
-
.
ACM
P
re
ss
.
1
2
9
5
-
1
3
0
2
.
[2
1
]
Be
lal,
M
.
A
.
,
Kh
a
li
f
a
,
I.
H.,
2
0
0
2
,
A
Co
m
p
a
ra
ti
v
e
S
tu
d
y
b
e
t
w
e
e
n
S
w
a
r
m
In
telli
g
e
n
c
e
a
n
d
G
e
n
e
ti
c
A
lg
o
rit
h
m
s.
Eg
y
p
ti
a
n
C
o
mp
u
ter
S
c
ien
c
e
J
o
u
rn
a
l
,
2
4
(1
)
.
[2
2
]
Ba
b
b
a
r
M
.
,
M
i
n
sk
e
r,
B.
,
G
o
ld
b
e
rg
,
D.
E.
,
2
0
0
2
,
A
M
u
lt
isc
a
le
I
sla
n
d
In
jec
ti
o
n
Ge
n
e
ti
c
A
lg
o
rith
m
fo
r
Op
ti
m
a
l
Gr
o
u
n
d
wa
ter
Rem
e
d
ia
ti
o
n
De
sig
n
.
In
:
2
0
0
2
W
a
ter
Re
so
u
rc
e
s
P
la
n
n
in
g
&
M
a
n
a
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e
m
e
n
t
Co
n
f
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re
n
c
e
,
Ro
a
n
o
k
e
,
V
A
.
Am
e
rica
n
S
o
c
iety
o
f
Civ
il
En
g
in
e
e
rs
(A
S
CE)
En
v
iro
n
m
e
n
tal
&
W
a
ter Res
o
u
rc
e
s In
stit
u
te (E
W
RI).
[2
3
]
W
e
i,
J.,
D.
T
.
L
e
e
,
2
0
0
4
,
A
Ne
w
A
p
p
ro
a
c
h
to
th
e
T
ra
v
e
ll
in
g
S
a
les
m
a
n
P
r
o
b
lem
Us
in
g
G
e
n
e
ti
c
A
l
g
o
rit
h
m
s
w
it
h
P
ri
o
rit
y
En
c
o
d
in
g
.
IEE
E
.
[2
4
]
Ng
u
y
e
n
,
H.
D.
I.
Yo
sh
ih
a
ra
,
K.
Ya
m
a
m
o
ri,
M
.
Ya
su
n
a
g
a
,
2
0
0
7
,
Im
p
le
m
e
n
tatio
n
o
f
a
n
Eff
e
c
ti
v
e
H
y
b
rid
GA
f
o
r
L
a
r
g
e
-
S
c
a
le
T
r
a
v
e
li
n
g
S
a
l
e
s
m
a
n
P
ro
b
lem
s.
IEE
E
T
ra
n
sa
c
ti
o
n
s
o
n
sy
ste
ms
,
M
A
N,
a
n
d
C
y
b
e
rn
e
ti
c
s
-
P
a
rt
B:
CYBERNET
ICS
,
3
7
(
1
).
[2
5
]
X
u
a
n
,
W
.
Y.
L
i,
2
0
0
5
,
S
o
lv
in
g
T
ra
v
e
li
n
g
S
a
les
m
a
n
P
ro
b
lem
b
y
Us
i
n
g
A
L
o
c
a
l
Ev
o
lu
ti
o
n
a
ry
A
lg
o
rit
h
m
.
IEE
E
.
[2
6
]
L
e
e
,
Z.
J.
2
0
0
4
,
A
Hy
b
rid
Al
g
o
rith
m
Ap
p
li
e
d
t
o
T
ra
v
e
li
n
g
S
a
le
sm
a
n
Pro
b
lem
.
P
ro
c
e
e
d
i
n
g
s
o
f
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h
e
2
0
0
4
IEE
E
In
tern
a
ti
o
n
a
l
C
o
n
f
e
re
n
c
e
o
n
Ne
two
rk
in
g
,
S
e
n
si
n
g
&
Co
n
tr
o
l.
[2
7
]
Ya
n
g
,
R.
,
1
9
9
7
,
S
o
lv
in
g
L
a
rg
e
T
ra
v
e
li
n
g
S
a
les
m
a
n
P
ro
b
lem
s
w
it
h
s
m
a
ll
P
o
p
u
latio
n
s.
Ge
n
e
ti
c
Al
g
o
rit
h
ms
in
En
g
i
n
e
e
rin
g
S
y
ste
ms
:
In
n
o
v
a
ti
o
n
s a
n
d
Ap
p
li
c
a
ti
o
n
s,
C
o
n
fer
e
n
c
e
P
u
b
li
c
a
ti
o
n
No
.
4
4
6
,
IEE
.
[2
8
]
S
m
it
h
,
K.
1
9
9
6
,
A
n
A
rg
u
m
e
n
t
f
o
r
A
b
a
n
d
o
n
i
n
g
th
e
T
ra
v
e
li
n
g
S
a
les
m
a
n
P
r
o
b
lem
a
s
a
Ne
u
ra
l
-
Ne
t
w
o
rk
Be
n
c
h
m
a
rk
.
IEE
E
T
ra
n
sa
c
ti
o
n
s
o
n
Ne
u
ra
l
Ne
t
wo
rk
s
,
7
(
6
).
[2
9
]
P
u
ll
a
n
,
W
.
2
0
0
3
,
A
d
a
p
ti
n
g
t
h
e
G
e
n
e
ti
c
A
lg
o
rit
h
m
to
th
e
T
ra
v
e
li
n
g
S
a
les
m
a
n
P
r
o
b
lem
.
IEE
E
.
[3
0
]
Ju
n
g
,
S
.
,
M
o
o
n
,
B.
R.
,
2
0
0
2
,
T
o
w
a
rd
M
in
im
a
l
Re
strictio
n
o
f
G
e
n
e
ti
c
En
c
o
d
in
g
a
n
d
Cro
ss
o
v
e
rs
f
o
r
th
e
Tw
o
-
Dim
e
n
sio
n
a
l
E
u
c
li
d
e
a
n
T
S
P
.
IEE
E
T
ra
n
sa
c
ti
o
n
s o
n
Ev
o
lu
t
io
n
a
r
y
Co
mp
u
t
a
ti
o
n
,
6
(6
).
[3
1
]
W
h
it
e
, C
.
M
.
,
Ye
n
,
G
.
,
2
0
0
4
,
A
Hy
b
rid
Ev
o
lu
ti
o
n
a
ry
A
lg
o
rit
h
m
f
o
r
T
ra
v
e
li
n
g
S
a
les
m
a
n
P
r
o
b
lem
,
IEE
E
.
[3
2
]
Bu
d
i
n
ich
,
M
.
,
1
9
9
6
,
A
S
e
l
f
-
Org
a
n
izin
g
Ne
u
ra
l
Ne
t
w
o
rk
f
o
r
th
e
Trav
e
li
n
g
S
a
les
m
a
n
P
ro
b
lem
T
h
a
t
Is
Co
m
p
e
ti
ti
v
e
w
it
h
S
im
u
late
d
A
n
n
e
a
li
n
g
.
Ne
u
ra
l
Co
mp
u
ti
n
g
8
,
4
1
6
-
4
2
4
.
[3
3
]
L
iu
,
G
.
Y.
He
,
Y.
F
a
n
g
,
Y.
Oiu
,
2
0
0
3
,
A
No
v
e
l
Ad
a
p
t
ive
S
e
a
rc
h
S
tra
teg
y
o
f
In
te
n
sifi
c
a
ti
o
n
a
n
d
Div
e
rs
if
ica
ti
o
n
i
n
T
a
b
u
S
e
a
rc
h
.
I
n
:
P
ro
c
e
e
d
in
g
s
o
f
th
e
IEE
E
In
tern
a
ti
o
n
a
l
Co
n
f
e
re
n
c
e
o
n
Ne
u
ra
l
Ne
tw
o
rk
s
a
n
d
S
ig
n
a
l
P
ro
c
e
ss
in
g
-
ICNN
S
P
’
0
3
,
IEE
E
1
.
4
2
8
-
4
3
1
.
[3
4
]
Bian
c
h
i,
L
.
,
Kn
o
w
les
,
J.,
Bo
w
l
e
r,
J.,
2
0
0
5
,
L
o
c
a
l
S
e
a
rc
h
f
o
r
th
e
P
r
o
b
a
b
il
isti
c
T
ra
v
e
li
n
g
S
a
le
s
m
a
n
P
ro
b
lem
:
Co
rre
c
ti
o
n
to
th
e
2
-
P
-
Op
t
a
n
d
1
-
s
h
if
t
A
lg
o
rit
h
m
s.
Eu
ro
p
e
a
n
J
o
u
rn
a
l
o
f
Op
e
ra
ti
o
n
a
l
Res
e
a
rc
h
1
6
2
(
1
)
2
0
6
-
2
1
9
.
[3
5
]
L
e
u
n
g
,
S
.
K.,
Jin
,
D.
H.,
X
u
,
B.
Z.
,
2
0
0
4
,
A
n
Ex
p
a
n
d
in
g
S
e
lf
-
Or
g
a
n
izin
g
Ne
u
ra
l
N
e
t
w
o
rk
f
o
r
th
e
T
ra
v
e
li
n
g
S
a
les
m
a
n
P
r
o
b
lem
.
Ne
u
ro
c
o
mp
u
t
in
g
6
2
.
2
6
7
-
2
9
2
.
[3
6
]
Re
in
e
lt
,
G
.
,
1
9
9
4
,
T
h
e
T
r
a
v
e
ll
i
n
g
S
a
les
m
a
n
:
Co
m
p
u
tatio
n
a
l
S
o
lu
ti
o
n
s
f
o
r
T
S
P
A
p
p
li
c
a
ti
o
n
s.
L
e
c
tu
re
No
tes
i
n
Co
mp
u
ter
S
c
ien
c
e
,
8
4
0
,
S
p
rin
g
e
r
-
V
e
rlag
.
[3
7
]
L
a
a
rh
o
v
e
n
,
P
.
V.,
A
a
rts,
L
.
E.
H.,
1
9
8
7
,
S
im
u
late
d
A
n
n
e
a
li
n
g
:
T
h
e
o
r
y
a
n
d
A
p
p
li
c
a
ti
o
n
s.
Kl
u
we
r
a
c
a
d
e
mic
Pu
b
li
s
h
e
rs
.
[3
8
]
F
iec
h
ter,
L
.
1
9
9
4
,
A
P
a
ra
ll
e
l
T
a
b
u
S
e
a
rc
h
A
lg
o
rit
h
m
f
o
r
L
a
r
g
e
T
ra
v
e
ll
in
g
S
a
les
m
a
n
P
ro
b
lem
s.
Disc
re
t
e
Ap
p
li
e
d
M
a
th
e
ma
ti
c
s a
n
d
Co
mb
in
a
to
ri
a
l
Op
e
ra
ti
o
n
s R
e
se
a
rc
h
a
n
d
Co
mp
u
t
e
r S
c
ien
c
e
,
5
1
.
[3
9
]
P
re
b
y
s,
E.
K.,
2
0
0
7
,
T
h
e
G
e
n
e
ti
c
A
l
g
o
rit
h
m
in
Co
m
p
u
ter
S
c
ien
c
e
.
M
IT
Un
d
e
rg
ra
d
u
a
te
J
o
u
rn
a
l
o
f
M
a
th
e
ma
ti
c
s,
e
e
.
sh
a
rif.
ir/~
p
o
s
h
tko
o
h
i
,
[
On
li
n
e
,
a
c
c
e
ss
e
d
2
0
0
7
]
.
[4
0
]
ww
w
.
g
e
o
c
it
ies
.
c
o
m
,
[
On
li
n
e
,
a
c
c
e
ss
e
d
2
0
0
7
]
,
A
G
e
n
e
ti
c
Kn
a
p
sa
c
k
P
ro
b
lem
S
o
lv
e
.
[4
1
]
G
o
d
e
f
ro
id
,
P
.
,
Kh
u
rsh
i
d
,
S
.
,
2
0
0
2
,
Ex
p
lo
rin
g
V
e
ry
L
a
rg
e
S
tate
S
p
a
c
e
s
U
sin
g
G
e
n
e
ti
c
A
l
g
o
rit
h
m
s.
J.
-
P.
Ka
to
e
n
a
n
d
P.
S
tev
e
n
s (
Ed
s.):
T
A
C
A
S
,
L
NC
S
2
2
8
0
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