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Sep
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201
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138
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3
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4
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1
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1
2
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[
1
3
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.
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I
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8
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Vo
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Sep
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ee
d
to
b
e
elim
in
at
ed
ar
e
as f
o
llo
w
s
[
1
4
]
:
1
1
1
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)
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o
s
(
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1
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2
1
4
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5
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2
3
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o
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(
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1
(
2
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2
3
(
4
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j
j
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3
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A
ll
tr
ip
le
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ar
m
o
n
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t f
r
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E
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u
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9
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th
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ar
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m
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,
i.e
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f
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f
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f
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17
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d
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25
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b
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e
f
u
n
ctio
n
o
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th
e
p
r
o
b
lem
i
s
)
2
3
(
7
5
1
...
)
(
N
f
(
4
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
PEDS
I
SS
N:
2088
-
8
694
Differ
en
tia
l E
vo
lu
tio
n
A
lg
o
r
ith
m
w
ith
Tr
ia
n
g
u
la
r
A
d
a
p
tive
C
o
n
tr
o
l P
a
r
a
mete
r
…
(
I
s
ma
il Yu
s
u
f
)
1383
3.
ADAP
T
I
VE
D
I
F
F
E
R
E
N
T
I
AL
E
VO
L
U
T
I
O
N
A
L
G
O
R
I
T
H
M
Dif
f
er
en
t
ial
ev
o
l
u
tio
n
al
g
o
r
ith
m
u
tili
z
ies
co
n
tr
o
l
p
ar
a
m
ete
r
s
,
i.e
.
,
m
u
tio
n
f
ac
to
r
(
F
)
an
d
cr
o
s
s
o
v
er
r
ate
(
CR
)
to
co
n
tr
o
l
p
er
tu
r
b
an
ce
an
d
i
m
p
r
o
v
e
co
n
v
er
g
en
ce
o
f
th
e
o
p
ti
m
izatio
n
p
r
o
ce
s
s
.
B
o
th
F
an
d
CR
h
a
v
e
v
alu
e
s
in
t
h
e
r
an
g
e
o
f
[
0
,
1
]
.
T
h
e
f
lo
w
ch
ar
t
o
f
p
r
o
p
o
s
ed
ad
ap
tiv
e
DE
to
o
p
tim
ize
N
s
w
itc
h
i
n
g
an
g
les
i
s
p
r
esen
ted
as in
F
ig
u
r
e
2
.
T
h
e
f
ir
s
t step
o
f
th
e
f
lo
w
c
h
ar
t is
g
e
n
er
atin
g
t
h
e
f
ir
s
t g
e
n
er
atio
n
o
f
p
o
p
u
latio
n
.
m
i
n
j
m
a
x
j
j
m
i
n
j
i
,
j
r
a
nd
)
1
(
(
5
)
w
h
er
e
minj
a
n
d
m
axj
ar
e
lo
w
e
r
an
d
u
p
p
er
b
o
u
n
d
s
o
f
t
h
e
s
w
it
h
in
g
a
n
g
les,
r
a
n
d
j
[
0
,
1
]
,
j
=
1
,
2
,
…,
N
;
i
=
1
,
2
,
…,
NP
,
an
d
NP
is
t
h
e
p
o
p
u
latio
n
s
ize.
E
v
al
u
ati
n
g
th
e
p
o
p
u
latio
n
is
co
n
d
u
cted
to
d
eter
m
in
e
w
h
ic
h
s
w
itc
h
in
g
a
n
g
le
s
o
f
t
h
e
c
u
r
r
en
t
g
en
er
atio
n
w
ill
b
e
t
h
e
b
est
c
an
d
id
ates
in
o
r
d
er
to
s
atis
f
y
th
e
co
n
s
tr
ain
ts
o
f
t
h
e
o
b
j
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tiv
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f
u
n
c
tio
n
.
)
(
)
1
(
)
1
(
b
e
st
j
b
e
st
j
f
f
,
)
1
(
,
)
1
(
j
i
b
e
s
t
j
(
6)
Ne
w
i
n
d
i
v
id
u
al
s
ar
e
ev
o
lv
ed
b
y
u
s
i
n
g
m
u
ta
tio
n
a
n
d
cr
o
s
s
o
v
er
o
p
er
ato
r
s
.
T
h
e
o
p
er
ato
r
s
ar
e
e
m
p
lo
y
ed
to
in
cr
ea
s
e
t
h
e
p
o
p
u
latio
n
d
iv
er
s
it
y
a
n
d
p
r
o
m
o
te
f
a
s
ter
co
n
v
er
g
en
ce
.
M
u
tat
io
n
o
p
er
atio
n
h
as
a
r
esp
o
n
s
ib
ilit
y
to
cr
ea
te
m
u
tan
t
i
n
d
iv
id
u
als,
w
h
i
le
cr
o
s
s
o
v
er
o
p
er
atio
n
is
to
cr
ea
te
tr
ial
i
n
d
iv
id
u
als.
T
h
e
i
n
d
i
v
id
u
al
s
i
n
t
h
is
ca
s
e
ar
e
th
e
s
w
itc
h
i
n
g
a
n
g
le
s
.
B
o
th
m
u
tatio
n
an
d
cr
o
s
s
o
v
er
p
r
o
ce
s
s
es
ar
e
s
h
o
w
n
as
i
n
Fi
g
u
r
e
3
.
I
n
th
e
m
u
tat
io
n
p
r
o
ce
s
s
,
th
e
i
n
d
ices
ra
,
rb
,
rc
,
rd
,
an
d
re
ar
e
f
i
v
e
m
u
t
u
all
y
d
is
ti
n
ct
i
n
teg
er
s
ta
k
en
r
a
n
d
o
m
l
y
f
r
o
m
{1
,
2
,
3
,
…,
NP
}.
Fu
r
t
h
er
,
t
h
e
i
n
teg
er
s
i
,
ra
,
rb
,
rc
,
rd
,
an
d
re
m
u
s
t
b
e
d
if
f
er
en
t.
I
n
s
elec
t
io
n
p
r
o
ce
s
s
a
s
s
ee
n
i
n
Fi
g
u
r
e
4
,
th
e
tr
ial
i
n
d
iv
id
u
al
s
(
t
i,
j
)
b
ec
o
m
e
t
h
e
b
est
n
ex
t
g
e
n
er
atio
n
i
n
d
iv
id
u
als
i
f
t
h
e
y
o
f
f
er
eq
u
al
o
r
lo
w
er
v
al
u
es
o
f
th
e
o
b
j
ec
tiv
e
f
u
n
ctio
n
th
a
n
t
h
o
s
e
o
f
th
eir
p
ar
en
t.
G
e
n
e
r
a
t
e
a
n
i
n
i
t
i
a
l
p
o
p
u
l
a
t
i
o
n
E
v
a
l
u
a
t
e
t
h
e
p
o
p
u
l
a
t
i
o
n
G
=
1
W
H
I
L
E
t
h
e
c
r
i
t
e
r
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i
s
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o
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s
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t
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s
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A
d
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d
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r
r
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M
u
t
a
t
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n
d
c
r
o
s
s
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v
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p
e
r
a
t
i
o
n
s
G
=
G
+
1
S
e
l
e
c
t
i
o
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o
p
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t
i
o
n
S
a
t
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s
f
i
e
d
s
w
i
t
c
h
i
n
g
a
n
g
l
e
s
Y
N
Fig
u
r
e
2
.
T
h
e
f
lo
w
c
h
ar
t o
f
ad
ap
tiv
e
DE
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2088
-
8
694
IJ
PEDS
Vo
l.
8
,
No
.
3
,
Sep
tem
b
er
2
0
1
7
:
1
3
8
1
–
1
3
8
8
1384
I
F
(
r
a
n
d
j
(
G
)
≤
C
R
i
(
G
)
)
o
r
(
j
=
j
r
a
n
d
)
F
O
R
j
=
1
t
o
N
t
i
,
j
(
G
)
=
α
i
,
j
(
G
)
t
i
,
j
(
G
)
=
α
r
a
,
j
(
G
)
+
(
F
i
(
G
)
(
α
r
b
,
j
(
G
)
–
α
r
c
,
j
(
G
)
)
+
F
i
(
G
)
(
α
r
d
,
j
(
G
)
–
α
r
e
,
j
(
G
)
)
Y
N
Fig
u
r
e
3
.
Mu
tatio
n
a
n
d
cr
o
s
s
o
v
er
o
p
er
atio
n
s
I
F
f
(
t
i
,
j
(
G
)
)
≤
f
(
α
i
,
j
(
G
)
)
F
O
R
i
=
1
t
o
N
P
α
i
,
j
(
G
+
1
)
=
α
i
,
j
(
G
)
α
i
,
j
(
G
+
1
)
=
t
i
,
j
(
G
)
Y
N
f
b
e
s
t
(
G
+
1
)
=
f
(
α
i
,
j
(
G
+
1
)
)
Fig
u
r
e
4
.
Selectio
n
o
p
er
atio
n
3
.
1
.
Ada
pta
t
i
o
ns
o
f
M
uta
t
io
n
a
nd
Cro
s
s
o
v
er
T
h
e
ad
ap
tatio
n
s
o
f
m
u
tat
io
n
a
n
d
cr
o
s
s
o
v
er
ar
e
e
m
p
lo
y
ed
to
av
o
id
in
e
f
f
ec
ti
v
e
eit
h
er
tr
ial
a
n
d
er
r
o
r
o
r
tu
n
in
g
p
r
o
ce
s
s
es
i
n
o
r
d
er
to
m
ee
t
th
e
b
est
v
al
u
es
o
f
F
an
d
CR
.
T
h
e
co
n
tr
o
l
p
ar
am
ater
s
at
t
h
e
G
th
-
g
en
er
at
io
n
,
i.e
.
,
F
i
(
G
)
an
d
CR
i
(
G
)
ar
e
ad
ap
te
d
b
y
u
s
i
n
g
t
r
ia
n
g
u
lar
p
r
o
b
ab
ilit
y
d
is
tr
ib
u
t
io
n
as
f
o
llo
w
s
:
)
X
X
X
)(X
r
a
n
d
-
(1
X
r
a
n
d
X
X
X
(X
r
a
n
d
X
X
m
a
x
j
m
a
x
j
m
a
x
j
m
i
n
G
i
o
t
h
e
r
w
i
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e
)(
if
)
)(
m
o
d
m
a
x
m
i
n
m
i
n
m
o
d
m
i
n
)
(
(
6
)
)
X
-
/
(
X
)
X
-
(X
m
i
n
m
a
x
m
i
n
m
o
d
(
7
)
T
h
e
tr
ian
g
u
lar
p
r
o
b
ab
ilit
y
d
is
tr
ib
u
tio
n
o
n
[
0
,
1
]
is
d
ef
in
ed
b
y
th
r
ee
v
al
u
es,
i.e
.
,
lo
w
er
l
i
m
it
X
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u
p
p
er
li
m
it
X
max
,
an
d
m
o
d
e
X
m
od
,
w
h
er
e
X
min
<
X
max
an
d
X
min
≤
X
mod
≤
X
max
.
T
h
er
e
ar
e
th
r
ee
p
o
s
s
ib
le
ca
s
es
[
1
5
]
as
s
h
o
w
n
in
Fig
u
r
e
5
,
i.e
.
,
(
i)
r
ig
h
t
tr
ian
g
u
lar
(
X
mod
=
X
min
)
,
(
i
i)
m
id
d
le
tr
ian
g
u
lar
(
X
min
<
X
mod
<
X
max
)
,
an
d
(
iii)
lef
t tr
ia
n
g
u
lar
(
X
mod
=
X
max
).
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
PEDS
I
SS
N:
2088
-
8
694
Differ
en
tia
l E
vo
lu
tio
n
A
lg
o
r
ith
m
w
ith
Tr
ia
n
g
u
la
r
A
d
a
p
tive
C
o
n
tr
o
l P
a
r
a
mete
r
…
(
I
s
ma
il Yu
s
u
f
)
1385
X
m
i
n
=
X
m
o
d
X
m
a
x
X
m
i
n
X
m
a
x
=
X
m
o
d
X
m
a
x
X
m
i
n
X
m
o
d
Fig
u
r
e
5
.
T
r
in
g
u
lar
p
r
o
b
ab
ilit
y
d
is
tr
ib
u
tio
n
s
4.
RE
SU
L
T
S AN
D
AN
AL
Y
SI
S
T
h
e
ad
ap
tiv
e
DE
is
ap
p
lied
to
m
i
n
i
m
ize
t
h
e
o
b
j
ec
tiv
e
f
u
n
cti
o
n
.
T
h
e
p
r
o
p
o
s
ed
DE
is
i
m
p
le
m
en
ted
b
y
u
s
i
n
g
M
A
T
L
A
B
s
o
f
t
w
ar
e
p
ac
k
ag
e
i
n
o
r
d
er
to
g
en
er
ate
S
H
E
P
W
M
s
w
itc
h
in
g
p
atter
n
s
.
T
h
er
e
ar
e
th
r
ee
ca
s
e
s
o
f
tr
ian
g
u
lar
(
as i
n
Fi
g
u
r
e
5
)
,
w
h
ic
h
ar
e
in
v
esti
g
ated
to
ad
ap
t b
o
th
F
an
d
CR
.
4
.
1
.
T
ria
ng
ula
r
Ada
ptiv
e
DE
T
h
er
e
ar
e
9
p
o
s
s
ib
le
co
m
b
in
a
tio
n
s
f
o
r
F
an
d
CR
.
i.e
.
,
(
F
=
r
ig
h
t
tr
ia
n
g
u
lar
,
CR
=
r
ig
h
t
t
r
ian
g
u
lar
)
,
(
F
=
r
ig
h
t
tr
ia
n
g
u
lar
,
CR
=
m
id
d
le
tr
ia
n
g
u
lar
)
,
(
F
=
r
i
g
h
t
tr
ian
g
u
lar
,
CR
=
le
f
t
tr
ia
n
g
u
lar
)
,
(
F
=
m
id
d
le
tr
ian
g
u
lar
,
CR
=
r
ig
h
t
tr
ia
n
g
u
lar
)
,
(
F
=
m
id
d
le
tr
ian
g
u
lar
,
CR
=
m
id
d
le
tr
ian
g
u
lar
)
,
(
F
=
m
id
d
le
tr
ian
g
u
lar
,
CR
=
le
f
t
tr
ia
n
g
u
lar
)
,
(
F
=
le
f
t
tr
ian
g
u
lar
,
CR
=
r
ig
h
t
tr
ian
g
u
la
r
)
,
(
F
=
lef
t
tr
ia
n
g
u
lar
,
CR
=
m
id
d
le
tr
ian
g
u
lar
)
,
an
d
(
F
=
lef
t
tr
ian
g
u
lar
,
CR
=
lef
t
tr
ian
g
u
lar
)
.
Fro
m
th
e
af
o
r
e
m
e
n
tio
n
ed
co
m
b
i
n
atio
n
s
,
th
er
e
is
o
n
l
y
o
n
e
co
m
b
i
n
atio
n
is
ap
p
licab
le
to
d
eter
m
in
e
SHEP
W
M
s
w
itc
h
i
n
g
p
atter
n
s
,
th
at
i
s
(
F
=
lef
t
tr
ian
g
u
lar
,
CR
=
r
ig
h
t
tr
ian
g
u
lar
)
.
T
h
e
o
th
er
co
m
b
i
n
a
tio
n
s
w
il
l r
esu
l
t in
t
h
e
n
o
n
-
co
r
v
er
g
e
n
ce
DE
.
Fig
u
r
e
6
s
h
o
w
s
F
a
n
d
CR
t
r
ac
k
in
g
o
f
t
h
e
tr
ia
n
g
u
lar
ad
ap
tiv
e
DE
in
o
r
d
er
to
s
atis
f
y
th
e
b
es
t
s
w
itc
h
in
g
an
g
le
s
f
o
r
th
e
SHE
P
W
M
w
it
h
N
=
9
,
M
=
0
.
0
5
,
a
n
d
f(
α
)
<
0
.
0
0
0
1
.
I
t
n
ee
d
s
2
5
g
en
er
atio
n
s
to
m
ee
t
th
e
o
p
ti
m
u
m
s
w
itc
h
i
n
g
a
n
g
l
es
as
s
h
o
w
n
in
Fi
g
u
r
e
7
.
T
h
e
tr
ian
g
u
lar
ad
ap
tiv
e
DE
is
f
as
ter
th
a
n
th
e
co
n
v
e
n
tio
n
al
DE
u
s
i
n
g
f
i
x
ed
F
an
d
CR
d
u
e
to
i
t
h
as
a
lar
g
e
j
u
m
p
to
t
h
e
o
p
ti
m
u
m
s
o
lu
t
io
n
s
.
A
cc
o
r
d
in
g
to
th
e
r
ep
o
r
t
in
[
1
4
]
,
th
e
b
est
f
i
x
e
d
F
an
d
CR
v
al
u
es
ar
e
0
.
2
6
an
d
1
.
0
0
,
r
esp
ec
tiv
el
y
.
Ho
w
e
v
er
,
it
n
ee
d
s
4
5
g
en
er
atio
n
s
to
s
o
l
v
e
t
h
is
s
w
i
tc
h
in
g
an
g
les
p
r
o
b
lem
,
as
s
h
o
w
n
i
n
Fig
u
r
e
8.
T
h
e
f
in
a
l
b
est
s
w
itc
h
i
n
g
a
n
g
le
s
to
b
e
f
o
u
n
d
b
y
b
o
th
DE
ar
e
α
1
=
1
1
.
7
4
2
3
°,
α
2
=
1
2
.
0
9
0
5
°,
α
3
=
2
3
.
7
3
4
2
°,
α
4
=
2
4
.
1
5
5
1
°,
α
5
=
3
5
.
7
2
8
2
°,
α
6
=
3
6
.
2
0
3
5
°,
α
7
=
4
7
.
7
2
9
1
°,
α
8
=
4
8
.
2
3
8
0
°,
an
d
α
9
=
5
9
.
7
3
9
8
°.
Fig
u
r
e
6
.
F a
n
d
C
R
ad
ap
tatio
n
s
0
5
10
15
20
25
0
.
2
0
.
3
0
.
4
0
.
5
0
.
6
0
.
7
0
.
8
0
.
9
1
g
e
n
e
r
a
t
i
o
n
C
R
,
F
CR
F
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2088
-
8
694
IJ
PEDS
Vo
l.
8
,
No
.
3
,
Sep
tem
b
er
2
0
1
7
:
1
3
8
1
–
1
3
8
8
1386
Fig
u
r
e
7
.
T
r
ian
g
u
lar
ad
ap
tiv
e
DE
tr
ac
k
in
g
t
h
e
b
est s
w
itc
h
i
n
g
an
g
les
Fig
u
r
e
8
.
C
o
n
v
e
n
tio
n
al
DE
tr
ac
k
in
g
t
h
e
b
est s
w
i
tch
i
n
g
an
g
le
s
4
.
2
.
SH
E
P
WM
Sw
it
ching
P
a
t
t
er
ns
T
h
er
e
ar
e
4
ty
p
es
o
f
p
o
s
s
ib
le
s
w
itc
h
in
g
p
atter
n
s
f
o
r
N
=
9
w
it
h
b
o
th
n
e
g
ati
v
e
an
d
p
o
s
iti
v
e
v
alu
e
s
o
f
M
,
as
p
r
esen
ted
in
Fig
u
r
e
9.
T
h
e
u
p
p
er
p
an
els
co
r
r
esp
o
n
d
w
ith
t
h
e
t
y
p
e
-
1
o
f
b
o
th
n
e
g
ati
v
e
an
d
p
o
s
itiv
e
s
w
itc
h
in
g
p
atter
n
s
,
a
n
d
o
t
h
er
p
an
els
co
r
r
esp
o
n
d
w
it
h
t
y
p
e
-
2
,
t
y
p
e
-
3
a
n
d
t
y
p
e
-
4
,
r
esp
ec
ti
v
el
y
.
T
h
e
n
e
g
ati
v
e
an
d
p
o
s
itiv
e
v
al
u
es o
f
M
ar
e
r
an
g
ed
b
et
w
ee
n
-
1
.
5
≤
M
≤
0
.
0
an
d
0
.
0
≤
M
≤
1
.
5
,
r
esp
ec
tiv
ely
.
T
h
e
co
m
p
u
tat
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n
p
r
o
ce
s
s
is
e
m
p
lo
y
ed
w
ith
a
s
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o
f
0
.
0
5
.
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h
e
n
u
m
b
er
o
f
g
en
er
atio
n
s
to
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e
n
er
ate
ev
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y
t
y
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f
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atter
n
s
i
s
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u
m
m
ar
ized
as
in
T
ab
le
1
.
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h
e
n
u
m
b
er
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g
en
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atio
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o
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1
0
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u
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d
w
it
h
1
0
0
%
s
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cc
e
s
s
r
ate.
0
5
10
15
20
25
30
0
20
40
60
80
100
120
140
160
180
g
e
n
e
r
a
t
i
o
n
a
n
g
l
e
(
d
e
g
r
e
e
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1
2
3
4
5
6
7
8
9
0
10
20
30
40
50
0
10
20
30
40
50
60
70
80
90
g
e
n
e
r
a
t
i
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n
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n
g
l
e
(
d
e
g
r
e
e
)
3
4
5
6
7
8
9
2
1
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
PEDS
I
SS
N:
2088
-
8
694
Differ
en
tia
l E
vo
lu
tio
n
A
lg
o
r
ith
m
w
ith
Tr
ia
n
g
u
la
r
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d
a
p
tive
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o
l P
a
r
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r
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s
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u
f
)
1387
Fig
u
r
e
9
.
SHEP
W
M
s
w
it
h
i
n
g
p
atter
n
s
f
o
r
N
=
9
w
it
h
M
n
e
g
ativ
e
an
d
p
o
s
iti
v
e
As
s
h
o
w
n
i
n
T
ab
le
1
,
th
e
ad
ap
tiv
e
DE
is
f
a
s
ter
t
h
a
n
t
h
e
co
n
v
e
n
tio
n
al
DE
i
n
g
en
er
ati
n
g
s
w
itc
h
in
g
p
atter
n
s
o
f
SHEP
W
M.
No
te
t
h
at
th
e
co
n
v
en
tio
n
al
DE
u
s
ed
f
i
x
ed
F
an
d
CR
o
f
0
.
2
6
an
d
1
.
0
0
,
r
esp
ec
tiv
ely
.
I
t
is
d
is
co
v
er
ed
th
at
t
h
e
tr
ia
n
g
u
l
ar
ad
ap
tiv
e
F
an
d
CR
i
m
p
r
o
v
e
th
e
s
p
ee
d
o
f
DE
to
r
ea
ch
t
h
e
o
p
tim
u
m
v
alu
e
s
o
f
-
1
.
2
-1
-
0
.
8
-
0
.
6
-
0
.
4
-
0
.
2
0
0
10
20
30
40
50
60
70
80
90
M
a
ng
l
e
(deg
ree)
0
0
.
2
0
.
4
0
.
6
0
.
8
1
1
.
2
0
10
20
30
40
50
60
70
80
90
M
a
ng
l
e
(deg
ree)
-
1
.
2
-1
-
0
.
8
-
0
.
6
-
0
.
4
-
0
.
2
0
0
10
20
30
40
50
60
70
80
90
M
a
ng
l
e
(deg
ree)
0
0
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2
0
.
4
0
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6
0
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8
1
1
.
2
0
10
20
30
40
50
60
70
80
90
M
a
ng
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e
(deg
ree)
-
1
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-
0
.
8
-
0
.
6
-
0
.
4
-
0
.
2
0
0
10
20
30
40
50
60
70
80
90
M
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ng
l
e
(deg
ree)
0
0
.
2
0
.
4
0
.
6
0
.
8
1
1
.
2
0
10
20
30
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50
60
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80
90
M
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ng
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e
(deg
ree)
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0
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0
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4
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0
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10
20
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50
60
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90
M
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e
(deg
ree)
0
0
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2
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4
0
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6
0
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8
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1
.
2
0
10
20
30
40
50
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70
80
90
M
a
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l
e
(deg
ree)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2088
-
8
694
IJ
PEDS
Vo
l.
8
,
No
.
3
,
Sep
tem
b
er
2
0
1
7
:
1
3
8
1
–
1
3
8
8
1388
s
w
itc
h
in
g
a
n
g
les
o
f
t
h
e
S
HE
P
W
M
p
r
o
b
lem
s
.
T
h
e
ad
ap
tiv
e
DE
r
eq
u
ir
es
less
n
u
m
b
er
o
f
g
e
n
er
atio
n
s
w
h
e
n
co
m
p
ar
ed
to
th
e
co
n
v
en
tio
n
al
DE
in
g
e
n
er
ati
n
g
S
HE
P
W
M
s
w
itc
h
i
n
g
p
atter
n
s
.
T
ab
le
1
.
Nu
m
b
er
o
f
Gen
er
atio
n
s
o
f
DE
P
a
t
t
e
r
n
s
N
u
mb
e
r
o
f
g
e
n
e
r
a
t
i
o
n
s
A
d
a
p
t
i
v
e
DE
C
o
n
v
e
n
t
i
o
n
a
l
DE
M
n
e
g
a
t
i
v
e
Ty
p
e
-
1
44
82
Ty
p
e
-
2
45
77
Ty
p
e
-
3
47
74
Ty
p
e
-
4
45
76
M
p
o
si
t
i
v
e
Ty
p
e
-
1
91
1
8
2
Ty
p
e
-
2
91
1
9
7
Ty
p
e
-
3
97
1
6
0
Ty
p
e
-
4
49
77
5.
CO
NCLU
SI
O
N
T
h
e
ap
p
licatio
n
o
f
tr
ian
g
u
lar
ad
ap
tiv
e
co
n
tr
o
l
p
ar
a
m
eter
h
as
a
n
ad
v
a
n
ta
g
e
i
n
in
cr
ea
s
in
g
th
e
DE
’
s
p
er
f
o
r
m
a
n
ce
to
s
o
lv
e
t
h
e
S
H
E
P
W
M
s
w
itc
h
i
n
g
a
n
g
les
p
r
o
b
le
m
.
I
t
r
ed
u
ce
s
h
al
f
o
f
t
h
e
n
u
m
b
er
o
f
g
en
er
atio
n
s
r
eq
u
ir
ed
to
m
ee
t
t
h
e
o
p
ti
m
u
m
v
alu
e
s
o
f
s
w
itc
h
i
n
g
an
g
le
s
in
co
m
p
ar
i
s
o
n
to
t
h
e
co
n
v
e
n
tio
n
al
DE
.
I
t
w
o
r
k
s
r
elativ
e
f
a
s
t
w
it
h
1
0
0
%
s
u
cc
e
s
s
r
ate
f
o
r
b
o
th
n
e
g
ati
v
e
an
d
p
o
s
itif
v
a
lu
e
s
o
f
m
o
d
u
latio
n
i
n
d
ex
.
T
h
ese
r
esu
l
ts
p
r
o
v
id
e
u
s
ef
u
ll
in
f
o
r
m
at
io
n
to
h
elp
d
esig
n
er
s
to
m
ak
e
a
d
ec
is
io
n
in
t
h
eir
SHEP
W
M
in
v
er
ter
f
o
r
r
ea
l
-
t
i
m
e
ap
p
licatio
n
s
RE
F
E
R
E
NC
E
S
[1
]
A.
A.
M
.
Ru
b
a
n
,
N
.
He
m
a
v
a
th
i,
a
n
d
N.
Ra
jes
w
a
ri,
“
Re
a
l
ti
m
e
H
a
r
m
o
n
ic
El
im
in
a
ti
o
n
P
W
M
Co
n
t
ro
l
f
o
r
V
o
l
tag
e
S
o
u
rc
e
In
v
e
rters
"
,
2
0
1
2
In
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
n
Ad
v
a
n
c
e
s
in
En
g
i
n
e
e
rin
g
,
S
c
ien
c
e
a
n
d
M
a
n
a
g
e
me
n
t
(
ICAE
S
M
),
p
p
.
4
7
9
-
4
8
4
,
2
0
1
2
.
[2
]
C.
Bu
c
c
e
ll
a
,
C.
Ce
c
a
ti
,
M
.
G
.
Cim
o
ro
n
i,
a
n
d
K.
Ra
z
i,
“
Re
a
l
-
ti
m
e
Ha
r
m
o
n
ics
El
im
in
a
ti
o
n
P
ro
c
e
d
u
re
s
f
o
r
Hig
h
-
P
o
w
e
r
Co
n
v
e
rters
"
,
2
0
1
3
IEE
E
I
n
ter
n
a
ti
o
n
a
l
W
o
rk
sh
o
p
o
n
I
n
telli
g
e
n
t
E
n
e
rg
y
S
y
ste
ms
(
IW
IES
),
p
p
.
1
7
9
-
1
8
4
,
2
0
1
3
.
[3
]
P.
S
.
Ka
ru
v
e
lam
a
n
d
M
.
Ra
jara
m
,
“
A
No
v
e
l
Tec
h
n
iq
u
e
f
o
r
Re
a
l
T
ime
Im
p
le
m
e
n
tatio
n
o
f
S
HE
P
W
M
i
n
S
in
g
le
P
h
a
se
M
a
tri
x
Co
n
v
e
rter"
,
T
e
h
n
ičk
i
Vj
e
sn
ik,
v
o
l
.
2
3
,
n
o
.
5
,
p
p
.
1
4
8
1
-
1
4
8
8
,
2
0
1
6
.
[4
]
R.
S
a
leh
i,
N.
F
a
ro
k
h
n
ia,
M
.
A
b
e
d
i,
a
n
d
S
.
H.
F
a
th
i,
“
El
im
in
a
ti
o
n
o
f
L
o
w
Ord
e
r
Ha
r
m
o
n
ics
in
M
u
lt
il
e
v
e
l
In
v
e
rt
e
r
Us
in
g
G
e
n
e
ti
c
A
lg
o
rit
h
m
"
,
J
o
u
rn
a
l
o
f
Po
we
r E
lec
tro
n
ics
,
v
o
l
.
1
1
,
n
o
.
2
,
p
p
.
1
3
2
-
1
3
9
,
2
0
1
1
.
[5
]
S.
A
.
Ka
n
d
il
,
A
.
A
.
A
li
,
A
.
El
S
a
m
a
h
y
,
S
.
M
.
W
a
sf
i,
a
n
d
O.
P
.
M
a
li
k
,
“
Ha
r
m
o
n
ic
Op
ti
m
iza
ti
o
n
i
n
V
o
l
tag
e
S
o
u
rc
e
In
v
e
rter
f
o
r
P
V
A
p
p
li
c
a
ti
o
n
Us
in
g
He
u
risti
c
A
lg
o
r
it
h
m
s
"
,
In
ter
n
a
ti
o
n
a
l
J
o
u
r
n
a
l
o
f
Eme
rg
in
g
El
e
c
tric
Po
we
r
S
y
ste
ms
,
v
o
l.
1
7
,
n
o
.
6
,
p
p
.
6
7
1
-
6
8
2
,
2
0
1
6
.
[6
]
R.
T
a
leb
,
M
.
H
e
lai
m
i,
D.
B
e
n
y
o
u
c
e
f
,
a
n
d
Z.
Bo
u
d
jem
a
,
“
Ge
n
e
ti
c
A
l
g
o
rit
h
m
A
p
p
li
c
a
ti
o
n
in
A
s
y
m
m
e
tri
c
a
l
9
-
Lev
e
l
In
v
e
rter"
,
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
Po
we
r E
lec
t
ro
n
ics
a
n
d
Dr
ive
S
y
ste
m (
IJ
PE
DS
),
v
o
l.
7
,
n
o
.
2
,
p
p
.
5
2
1
-
5
3
0
,
2
0
1
6
.
[7
]
S
.
Ro
su
,
C.
Ra
d
o
i,
A
.
F
lo
re
sc
u
,
P
.
G
u
g
li
e
l
m
i,
a
n
d
M
.
P
a
sto
re
ll
i
,
“
T
h
e
A
n
a
l
y
sis
o
f
th
e
S
o
lu
ti
o
n
s
f
o
r
Ha
r
m
o
n
ic
El
im
in
a
ti
o
n
P
W
M
Bi
p
o
lar
W
a
v
e
f
o
r
m
w
it
h
a
S
p
e
c
ialize
d
Diff
e
r
e
n
ti
a
l
Ev
o
lu
t
io
n
A
lg
o
r
it
h
m
"
,
2
0
1
2
1
3
th
In
ter
n
a
t
io
n
a
l
C
o
n
fer
e
n
c
e
o
n
O
p
ti
miza
ti
o
n
o
f
El
e
c
trica
l
a
n
d
El
e
c
tro
n
ic E
q
u
ip
me
n
t
(
OPT
IM
),
p
p
.
8
1
4
-
8
2
1
,
2
0
1
2
.
[8
]
P
.
Ja
m
u
n
a
a
n
d
C.
C.
A
.
Ra
j
a
n
,
“
H
y
b
rid
T
rig
o
n
o
m
e
tri
c
Di
ff
e
r
e
n
ti
a
l
Ev
o
lu
ti
o
n
f
o
r
Op
ti
m
i
z
in
g
Ha
r
m
o
n
ic
Distrib
u
ti
o
n
"
,
I
n
ter
n
a
ti
o
n
a
l
J
o
u
r
n
a
l
o
f
Co
m
p
u
ter
a
n
d
El
e
c
trica
l
E
n
g
in
e
e
rin
g
,
v
o
l.
5
,
n
o
.
5
,
p
p
.
4
8
2
-
4
8
6
,
2
0
1
3
.
[9
]
A.
M
.
Am
jad
,
Z.
S
a
lam
,
a
n
d
A
.
M.
A
.
S
a
if
,
“
A
p
p
li
c
a
ti
o
n
o
f
Diffe
re
n
ti
a
l
Ev
o
lu
ti
o
n
f
o
r
Ca
sc
a
d
e
d
M
u
lt
il
e
v
e
l
VSI
w
it
h
Ha
r
m
o
n
ics
El
im
in
a
ti
o
n
P
W
M
S
w
it
c
h
in
g
"
,
In
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
El
e
c
trica
l
P
o
we
r
&
En
e
rg
y
S
y
ste
ms
,
v
o
l.
6
4
,
p
p
.
4
4
7
-
4
5
6
,
2
0
1
5
.
[1
0
]
F
.
Ch
a
b
n
i
,
R.
T
a
leb
,
a
n
d
A
.
M
e
l
lak
h
i,
“
El
im
in
a
ti
o
n
o
f
Ha
r
m
o
n
ic
s
in
M
o
d
if
ied
5
-
L
e
v
e
l
CHB
In
v
e
rter
Us
in
g
DE
A
l
g
o
rit
h
m
"
,
M
e
d
it
e
rr
a
n
e
a
n
J
o
u
r
n
a
l
o
f
M
o
d
e
li
n
g
a
n
d
S
im
u
la
ti
o
n
,
v
o
l.
6
,
p
p
.
2
3
-
3
3
,
2
0
1
6
.
[1
1
]
Y.
F
a
n
,
Q.
L
ian
g
,
C.
L
iu
,
a
n
d
X
.
Ya
n
,
“
S
e
lf
–
A
d
a
p
ti
n
g
Co
n
tr
o
l
P
a
ra
m
e
ter
s
w
it
h
M
u
lt
i
-
P
a
re
n
t
Cro
ss
o
v
e
r
in
Diff
e
r
e
n
ti
a
l
Ev
o
lu
ti
o
n
A
lg
o
rit
h
m
"
,
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
C
o
mp
u
ti
n
g
S
c
ien
c
e
a
n
d
M
a
th
e
ma
ti
c
s,
v
o
l.
6
,
n
o
.
1
,
p
p
.
40
-
4
8
,
2
0
1
5
.
[1
2
]
M
.
L
o
c
a
telli
a
n
d
M
.
V
a
sil
e
,
“
(N
o
n
)
C
o
n
v
e
rg
e
n
c
e
Re
su
lt
s
f
o
r
th
e
Diff
e
re
n
ti
a
l
Ev
o
lu
ti
o
n
M
e
t
h
o
d
"
,
Op
ti
miza
ti
o
n
L
e
tt
e
rs
,
v
o
l.
9
,
n
o
.
3
,
p
p
.
4
1
3
-
4
2
5
,
2
0
1
5
.
[1
3
]
G
.
S
q
u
il
lero
a
n
d
A
.
T
o
n
d
a
,
“
Div
e
rg
e
n
c
e
o
f
Ch
a
ra
c
ter
a
n
d
P
re
m
a
tu
re
Co
n
v
e
rg
e
n
c
e
:
A
S
u
rv
e
y
o
f
M
e
t
h
o
d
o
l
o
g
ies
f
o
r
P
r
o
m
o
ti
n
g
Div
e
rsit
y
i
n
Ev
o
lu
ti
o
n
a
r
y
Op
ti
m
iz
a
ti
o
n
"
,
In
f
o
rm
a
ti
o
n
S
c
ien
c
e
s,
v
o
l.
3
2
9
,
p
p
.
7
8
2
-
7
9
9
,
2
0
1
6
.
[1
4
]
A
.
Hie
n
d
ro
,
“
M
u
lt
i
p
le
S
w
it
c
h
in
g
P
a
tt
e
rn
s
f
o
r
S
HE
P
W
M
In
v
e
rters
Us
in
g
Di
ff
e
re
n
ti
a
l
Ev
o
lu
ti
o
n
A
lg
o
rit
h
m
s
"
,
In
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
P
o
we
r E
lec
tro
n
ics
a
n
d
Dr
ive
S
y
ste
m (
IJ
PE
DS
),
v
o
l.
1
,
n
o
.
2
,
p
p
.
9
4
-
1
0
3
,
2
0
1
1
.
[1
5
]
W.
E.
S
tein
a
n
d
M
.
F
.
Ke
b
li
s,
“
A
Ne
w
M
e
th
o
d
t
o
S
im
u
late
t
h
e
T
rian
g
u
lar
Distrib
u
ti
o
n
"
,
M
a
th
e
ma
ti
c
a
l
a
n
d
Co
mp
u
ter
M
o
d
e
li
n
g
,
v
o
l
.
4
9
,
p
p
.
1
1
4
3
-
1
1
4
7
,
2
0
0
9
.
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