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12
,
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.
1
,
Octo
b
er
201
8
,
p
p
.
87
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f
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o
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p
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s.
T
h
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EAs
a
re
th
e
p
o
p
u
lati
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b
a
se
d
a
lg
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h
m
s
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a
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m
a
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(s)
f
ro
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a
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it
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se
t
o
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c
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n
d
id
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tes
so
lu
ti
o
n
s
k
n
o
w
n
a
s
p
o
p
u
lati
o
n
.
T
h
is
p
o
p
u
lati
o
n
is
t
o
b
e
in
it
ializ
e
d
a
t
f
irst
b
e
f
o
re
th
e
e
v
o
lu
ti
o
n
o
f
th
e
a
lg
o
r
it
h
m
sta
rts.
T
h
e
re
e
x
ists
d
iff
e
r
e
n
t
w
a
y
s
to
in
it
ialize
th
is
p
o
p
u
la
ti
o
n
.
Un
d
e
rs
tan
d
i
n
g
a
n
d
c
h
o
o
si
n
g
th
e
rig
h
t
p
o
p
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lati
o
n
in
it
ializa
ti
o
n
tec
h
n
iq
u
e
f
o
r
th
e
g
iv
e
n
p
ro
b
lem
is
a
d
iff
i
c
u
lt
tas
k
f
o
r
th
e
re
se
a
rc
h
e
rs
a
n
d
p
ro
b
lem
so
lv
e
rs.
T
o
a
ll
e
v
iate
th
is
issu
e
,
th
is
p
a
p
e
r
is
f
ra
m
e
d
w
it
h
tw
o
o
b
jec
ti
v
e
s.
T
h
e
f
irst
o
b
jec
ti
v
e
is
to
p
re
se
n
t
t
h
e
d
e
tails
o
f
v
a
rio
u
s
P
o
p
u
latio
n
I
n
it
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ti
o
n
(
P
I)
tec
h
n
iq
u
e
s
o
f
EA
s,
f
o
r
th
e
re
a
d
e
rs
to
g
iv
e
b
rie
f
d
e
sc
rip
ti
o
n
o
f
a
ll
th
e
P
I
tec
h
n
i
q
u
e
s.
T
h
e
se
c
o
n
d
o
b
jec
ti
v
e
is
to
p
re
se
n
t
th
e
ste
p
s
a
n
d
e
m
p
iri
c
a
l
c
o
m
p
a
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n
o
f
th
e
re
su
lt
s
o
f
tw
o
d
if
f
e
re
n
t
P
I
tec
h
n
iq
u
e
s
im
p
le
m
e
n
ted
f
o
r
Diff
e
r
e
n
ti
a
l
Ev
o
lu
ti
o
n
(DE)
a
lg
o
rit
h
m
.
T
h
e
o
re
ti
c
a
l
in
sig
h
ts
a
n
d
e
m
p
iri
c
a
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su
lt
s o
f
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P
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h
n
i
q
u
e
s are
p
re
se
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ted
in
th
is
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a
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r
.
K
ey
w
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r
d
s
:
Dif
f
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t
ial
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A
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ith
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s
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itializatio
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R
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Op
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ased
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S
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.
Al
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rig
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re
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C
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p
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A
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en
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cb
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s
tu
d
e
n
ts
.
a
m
r
ita.
ed
u
1.
I
NT
RO
D
UCT
I
O
N
An
o
p
ti
m
iza
tio
n
p
r
o
b
le
m
is
a
p
r
o
b
lem
to
w
h
ic
h
t
h
e
b
est
p
o
s
s
ib
le
o
p
ti
m
al
s
o
l
u
tio
n
is
to
b
e
s
ea
r
ch
ed
f
r
o
m
a
s
e
t
o
f
f
ea
s
ib
le
o
n
es
in
t
h
e
p
o
p
u
latio
n
s
p
ac
e.
T
h
er
e
e
x
is
t
n
u
m
er
o
u
s
o
p
ti
m
iz
atio
n
al
g
o
r
ith
m
s
i
n
Ma
th
e
m
atics
a
n
d
C
o
m
p
u
ter
S
cien
ce
.
T
h
e
E
v
o
lu
tio
n
ar
y
C
o
m
p
u
tat
io
n
(
EC
)
f
ield
o
f
C
o
m
p
u
ter
Scien
ce
h
a
s
a
s
et
o
f
alg
o
r
it
h
m
s
k
n
o
w
n
as
E
v
o
lu
tio
n
ar
y
A
l
g
o
r
ith
m
s
(
EAs
)
f
o
r
s
o
lv
in
g
o
p
ti
m
izatio
n
p
r
o
b
le
m
s
.
T
h
e
y
ar
e
th
e
m
o
s
t
w
id
el
y
u
s
ed
to
o
ls
to
s
o
l
v
e
r
ea
l
ti
m
e
o
p
ti
m
izatio
n
p
r
o
b
le
m
s
[
1
-
3
]
.
I
t
is
a
f
a
m
i
l
y
o
f
alg
o
r
ith
m
s
u
s
ed
f
o
r
g
lo
b
al
o
p
ti
m
izat
io
n
i
n
s
p
ir
ed
f
r
o
m
Dar
w
in
ā
s
t
h
eo
r
y
o
f
n
atu
r
al
s
elec
tio
n
.
Nat
u
r
al
s
e
lectio
n
is
t
h
e
p
r
o
ce
s
s
b
y
w
h
ic
h
t
h
e
f
it
test
ca
n
d
id
ate
w
i
ll
s
u
r
v
i
v
e
lo
n
g
er
.
EAs
ar
e
p
o
p
u
latio
n
b
ased
o
p
ti
m
izatio
n
al
g
o
r
ith
m
s
.
I
n
E
A
s
,
a
p
o
p
u
latio
n
o
f
ca
n
d
id
ate
s
o
l
u
tio
n
s
i
s
g
e
n
er
ated
in
itia
ll
y
a
n
d
n
e
w
s
o
l
u
tio
n
s
ar
e
g
e
n
er
ated
iter
ativ
el
y
b
y
f
o
llo
w
in
g
t
h
e
e
v
o
lu
t
io
n
ar
y
p
r
o
ce
s
s
.
I
n
s
ta
n
ce
s
o
f
EAs
ar
e
E
v
o
lu
tio
n
Stra
te
g
ies
(
ES
)
,
Gen
etic
P
r
o
g
r
a
m
m
i
n
g
(
GP
)
,
E
v
o
lu
tio
n
ar
y
p
r
o
g
r
a
m
m
in
g
(
EP
)
,
Gen
etic
A
l
g
o
r
ith
m
(
GA
)
an
d
Di
f
f
er
e
n
tial
E
v
o
l
u
ti
o
n
(
DE
)
.
I
n
w
h
ic
h
,
DE
is
a
r
ec
en
t
ad
d
itio
n
to
EA
f
a
m
il
y
.
A
ll
th
e
s
e
in
s
tan
ce
s
f
o
llo
w
t
h
e
g
en
er
ate
-
a
n
d
-
te
s
t
s
tr
ateg
y
o
f
p
r
o
b
lem
s
o
lv
i
n
g
[
4
]
.
I
n
EA,
th
e
p
o
p
u
latio
n
i
s
a
s
et
o
f
p
o
s
s
ib
le
s
o
lu
tio
n
s
f
o
r
a
g
i
v
en
p
r
o
b
lem
.
E
ac
h
s
o
l
u
tio
n
i
s
r
ep
r
esen
ted
as
a
v
ec
to
r
(
also
ter
m
ed
as
c
h
r
o
m
o
s
o
m
e)
.
E
ac
h
ch
r
o
m
o
s
o
m
e
co
n
s
is
t
s
o
f
attr
ib
u
tes
a
n
d
ea
ch
attr
ib
u
te
s
h
o
ld
s
o
m
e
v
al
u
e.
T
h
e
ev
o
l
u
tio
n
ar
y
s
ea
r
ch
o
f
a
n
EA
s
tar
ts
w
i
t
h
a
n
I
n
itial
P
o
p
u
latio
n
(
IP
)
o
f
s
o
lu
tio
n
s
.
I
n
g
e
n
er
al,
th
e
IP
is
g
e
n
er
ated
r
an
d
o
m
l
y
if
n
o
p
r
o
b
lem
s
p
ec
i
f
ic
in
f
o
r
m
at
io
n
is
k
n
o
w
n
.
Oth
er
w
i
s
e,
p
r
o
b
lem
s
p
ec
i
f
ic
h
eu
r
i
s
tics
ca
n
b
e
ad
d
ed
f
o
r
th
e
p
o
p
u
latio
n
i
n
itia
lizatio
n
.
C
o
n
s
id
er
i
n
g
t
h
ese
f
ac
t
s
,
th
er
e
ex
is
t
d
i
f
f
er
e
n
t
Evaluation Warning : The document was created with Spire.PDF for Python.
ļ²
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J
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n
g
&
C
o
m
p
Sci,
Vo
l.
12
,
No
.
1
,
Octo
b
er
2
0
1
8
:
87
ā
94
88
p
o
p
u
latio
n
i
n
itializat
io
n
(
PI
)
t
ec
h
n
iq
u
es
in
th
e
liter
at
u
r
e
o
f
EC
co
m
m
u
n
it
y
.
T
h
i
s
i
n
clu
d
e
s
alg
o
r
ith
m
s
p
ec
if
ic
an
d
p
r
o
b
lem
s
p
ec
i
f
ic
PI
tech
n
i
q
u
es.
E
ac
h
PI
tech
n
iq
u
e
h
a
s
it
s
o
wn
c
h
ar
ac
ter
is
tics
.
T
h
e
o
b
j
ec
ti
v
e
o
f
th
i
s
p
ap
er
is
to
p
r
ese
n
t,
in
d
etail,
d
if
f
er
e
n
t
PI
tech
n
iq
u
es
o
f
EAs
.
Fu
r
th
er
,
in
o
r
d
er
to
p
r
o
v
id
e
ex
p
er
i
m
en
ta
l
ev
id
en
ce
s
to
th
e
r
ea
d
er
,
th
is
p
ap
er
also
d
is
cu
s
s
es
th
e
r
es
u
lt
s
o
b
tain
ed
b
y
i
m
p
le
m
e
n
ti
n
g
t
w
o
d
if
f
er
en
t
PI
tec
h
n
iq
u
es
ap
p
lied
f
o
r
DE
alg
o
r
ith
m
.
T
h
e
th
eo
r
etica
l
i
n
f
o
r
m
atio
n
ab
o
u
t
PI
tec
h
n
iq
u
e
s
a
n
d
th
e
e
m
p
ir
ical
r
es
u
lt
s
o
b
tain
ed
u
s
in
g
DE
a
lg
o
r
it
h
m
p
r
o
v
id
ed
in
th
is
p
ap
er
w
o
u
ld
d
ef
in
i
tel
y
h
elp
t
h
e
r
esear
ch
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s
in
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co
m
m
u
n
it
y
to
u
n
d
er
s
t
an
d
th
e
i
m
p
o
r
tan
ce
o
f
PI
f
o
r
s
o
lv
i
n
g
t
h
e
g
i
v
e
n
p
r
o
b
le
m
.
T
h
e
p
ap
er
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o
r
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an
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a
s
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w
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.
Sectio
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2
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o
r
k
o
f
E
v
o
lu
tio
n
ar
y
A
l
g
o
r
ith
m
,
Sectio
n
3
in
tr
o
d
u
ce
s
t
h
e
p
o
p
u
latio
n
co
n
ce
p
ts
o
f
EAs
a
n
d
Sect
io
n
4
p
r
ese
n
ts
t
h
e
d
etail
s
o
f
PI
tech
n
iq
u
es.
T
h
e
Sect
io
n
5
ex
p
lain
s
th
e
d
es
ig
n
o
f
ex
p
er
i
m
en
t
an
d
th
e
Sectio
n
6
p
r
ese
n
ts
t
h
e
e
m
p
ir
ical
r
es
u
lt
s
o
f
PI
tech
n
iq
u
es o
n
DE
al
g
o
r
it
h
m
.
Fin
al
l
y
,
t
h
e
Sectio
n
7
co
n
clu
d
es t
h
e
p
ap
er
s
.
2.
E
VO
L
U
T
I
O
N
ARY
A
L
G
O
R
I
T
H
M
S
T
o
s
o
lv
e
an
o
p
ti
m
izatio
n
p
r
o
b
le
m
,
a
s
et
o
f
f
e
w
p
o
s
s
ib
le
s
o
lu
tio
n
s
(
ca
n
d
id
ates)
to
th
e
p
r
o
b
lem
is
cr
ea
ted
in
itiall
y
a
n
d
g
i
v
e
n
as
i
n
p
u
t
to
th
e
EA.
T
h
e
n
at
u
r
e
o
f
EA
is
to
g
en
er
ate
n
e
w
ca
n
d
id
a
tes
(
ch
ild
r
en
)
f
r
o
m
th
e
s
elec
ted
ca
n
d
id
ates
(
p
ar
en
ts
)
o
f
th
e
in
itia
l
p
o
p
u
latio
n
,
an
d
allo
w
th
e
f
itte
s
t
ca
n
d
id
ates
am
o
n
g
t
h
e
p
ar
en
ts
an
d
ch
ild
r
en
to
s
u
r
v
i
v
e
f
o
r
th
e
n
ex
t
g
en
er
atio
n
.
T
h
is
p
h
en
o
m
en
o
n
is
d
er
iv
ed
f
r
o
m
t
h
e
ā
s
u
r
v
iva
l
o
f
th
e
fitt
est
ā
co
n
ce
p
t
o
f
Dar
w
i
n
ā
s
n
at
u
r
al
ev
o
lu
tio
n
t
h
eo
r
y
.
T
h
e
ca
n
d
id
ates
i
n
a
p
o
p
u
latio
n
ar
e
s
u
p
p
lied
w
it
h
l
i
m
ited
r
eso
u
r
ce
s
i
n
an
en
v
ir
o
n
m
e
n
t.
Un
d
er
th
e
e
n
v
ir
o
n
m
en
t
p
r
ess
u
r
e,
th
e
in
d
i
v
id
u
al
s
m
u
s
t
co
m
p
ete
ea
ch
o
th
er
f
o
r
th
e
r
eso
u
r
ce
s
w
h
ic
h
r
esu
lts
i
n
s
u
r
v
iv
al
o
f
th
e
f
itter
i
n
d
iv
id
u
a
ls
.
B
ased
o
n
th
e
o
b
j
ec
tiv
e
o
f
th
e
ch
o
s
e
n
p
r
o
b
lem
,
a
f
it
n
es
s
f
u
n
ctio
n
is
d
e
f
i
n
ed
t
o
m
ea
s
u
r
e
th
e
f
i
tn
e
s
s
o
f
ea
c
h
ca
n
d
id
ate
in
th
e
p
o
p
u
latio
n
.
B
ased
o
n
t
h
e
f
itn
e
s
s
v
alu
e
s
f
itte
s
t
ca
n
d
id
ates
ar
e
c
h
o
s
en
f
o
r
t
h
e
n
e
x
t
g
en
er
atio
n
.
T
h
e
ev
o
lu
t
io
n
ar
y
o
p
er
ato
r
s
u
s
ed
d
u
r
in
g
t
h
e
EA
p
r
o
ce
s
s
ar
e
R
ec
o
m
b
in
at
io
n
an
d
m
u
ta
tio
n
.
R
ec
o
m
b
i
n
atio
n
i
s
ap
p
lied
b
et
w
ee
n
t
w
o
o
r
m
o
r
e
s
elec
ted
ca
n
d
id
ates,
w
h
ic
h
ar
e
ca
lled
as
p
ar
en
t
ca
n
d
id
ates.
R
ec
o
m
b
in
atio
n
r
es
u
lts
i
n
o
n
e
o
r
m
o
r
e
n
e
w
ca
n
d
id
ates.
Mu
tatio
n
is
ap
p
lied
o
n
a
s
i
n
g
le
ca
n
d
id
ate
an
d
r
es
u
lts
a
n
e
w
ca
n
d
id
ate.
Mu
tatio
n
a
n
d
r
ec
o
m
b
i
n
atio
n
o
n
s
elec
ted
ca
n
d
id
ates
lead
s
to
th
e
cr
ea
tio
n
o
f
n
e
w
ca
n
d
id
ates
(
o
f
f
s
p
r
in
g
s
)
.
He
n
ce
th
e
y
ar
e
n
a
m
ed
as
v
ar
iatio
n
o
p
er
ato
r
s
.
T
h
en
th
e
s
elec
tio
n
o
p
er
ato
r
d
ec
i
d
es
th
e
s
u
r
v
i
v
o
r
s
f
o
r
n
e
x
t
g
e
n
er
atio
n
f
r
o
m
th
e
p
o
o
l
o
f
p
ar
en
ts
a
n
d
o
f
f
s
p
r
in
g
s
.
T
h
e
v
ar
iatio
n
a
n
d
s
elec
tio
n
o
p
er
atio
n
s
ar
e
ca
r
r
ied
o
u
t
iter
ativ
e
l
y
u
n
til
t
h
e
al
g
o
r
ith
m
r
ea
ch
e
s
a
u
s
er
d
ef
in
ed
s
to
p
p
in
g
cr
iter
ia.
I
t
is
v
er
y
w
ell
e
v
id
e
n
t t
h
at,
a
f
ter
e
v
er
y
iter
atio
n
,
t
h
e
b
est
ca
n
d
id
at
e
in
t
h
e
p
o
p
u
latio
n
m
o
v
e
s
to
w
ar
d
s
th
e
g
lo
b
al
o
p
t
i
m
al
s
o
l
u
tio
n
[
4
]
.
T
h
e
g
en
er
a
l
s
tr
u
ctu
r
e
o
f
EA
is
s
h
o
w
n
i
n
Fi
g
u
r
e
1
.
A
s
it
is
n
o
ted
f
r
o
m
t
h
e
al
g
o
r
ith
m
ic
s
tr
u
ctu
r
e
s
h
o
w
n
i
n
Fi
g
u
r
e
1
,
th
e
EA
co
m
e
s
u
n
d
er
th
e
ca
teg
o
r
y
o
f
g
e
n
er
ate
-
a
n
d
-
test
alg
o
r
ith
m
s
.
T
h
e
m
o
s
t
i
m
p
o
r
tan
t
co
m
p
o
n
e
n
ts
o
f
EA
ar
e
1
)
C
an
d
id
ate
R
ep
r
esen
tat
io
n
2
)
Po
p
u
latio
n
I
n
itializatio
n
3
)
Fit
n
es
s
f
u
n
c
tio
n
4
)
P
ar
en
t
s
elec
t
io
n
m
ec
h
an
i
s
m
5
)
Var
iatio
n
o
p
er
ato
r
s
an
d
6
)
Su
r
v
i
v
o
r
s
elec
tio
n
m
ec
h
an
i
s
m
3.
P
O
P
UL
AT
I
O
N
P
o
p
u
latio
n
cr
ea
tes
t
h
e
u
n
it
o
f
ev
o
lu
t
io
n
f
o
r
t
h
e
EA
.
T
h
e
ca
n
d
id
ates
(
in
d
i
v
id
u
al
s
o
r
c
h
r
o
m
o
s
o
m
es)
i
n
a
p
o
p
u
latio
n
r
ep
r
esen
t
p
o
s
s
ib
le
s
o
lu
tio
n
s
o
f
th
e
p
r
o
b
le
m
to
b
e
s
o
lv
ed
.
T
h
e
n
u
m
b
er
o
f
p
ar
a
m
eter
s
r
ep
r
esen
ted
in
a
ca
n
d
id
ate
is
eq
u
al
to
th
e
d
i
m
e
n
s
io
n
o
f
th
e
p
r
o
b
le
m
.
T
h
is
is
d
e
n
o
ted
as
c
h
r
o
m
o
s
o
m
e
le
n
g
t
h
.
A
l
l
t
h
e
ca
n
d
id
ates
i
n
a
p
o
p
u
latio
n
w
il
l
h
a
v
e
eq
u
al
len
g
t
h
.
I
t
is
n
ec
es
s
ar
y
to
i
n
itialize
/d
ef
i
n
e
a
p
o
p
u
latio
n
b
y
s
p
ec
if
y
in
g
th
e
n
u
m
b
er
o
f
in
d
iv
id
u
al
s
(
p
o
p
u
latio
n
s
ize)
i
n
t
h
e
p
o
p
u
latio
n
.
Mo
s
t
o
f
th
e
c
ases
t
h
e
p
o
p
u
latio
n
s
ize
o
f
t
h
e
EA
ar
e
k
ep
t
co
n
s
ta
n
t.
T
h
is
lead
s
to
th
e
co
m
p
etiti
o
n
f
o
r
l
i
m
ited
r
eso
u
r
ce
s
b
et
wee
n
t
h
e
ca
n
d
id
ates.
Fo
r
in
s
tan
ce
s
,
t
h
e
f
itte
s
t
ca
n
d
id
ate
o
f
a
p
o
p
u
latio
n
i
s
s
elec
te
d
f
o
r
n
e
x
t
g
e
n
er
atio
n
a
n
d
t
h
e
w
o
r
s
t
ca
n
d
id
ate
i
s
r
ep
lace
d
b
y
th
e
b
est
ca
n
d
id
a
te.
T
h
e
n
u
m
b
er
o
f
d
if
f
er
en
t
ca
n
d
i
d
ates
p
r
esen
t
i
n
a
p
o
p
u
latio
n
s
p
ec
if
ies
t
h
e
d
iv
er
s
it
y
o
f
t
h
e
p
o
p
u
latio
n
.
T
h
e
p
o
p
u
latio
n
w
it
h
s
et
o
f
p
o
s
s
ib
le
s
o
lu
tio
n
s
i
s
also
ter
m
ed
as s
o
lu
tio
n
s
p
ac
e.
T
h
e
d
i
m
en
s
io
n
o
f
t
h
e
s
o
lu
tio
n
s
p
a
ce
is
t
h
e
d
i
m
en
s
io
n
o
f
t
h
e
p
r
o
b
lem
to
b
e
s
o
lv
ed
.
T
h
e
EA
,
s
tar
t
t
h
e
s
ea
r
c
h
o
f
g
lo
b
al
o
p
ti
m
al
s
o
l
u
tio
n
f
r
o
m
th
e
in
i
tial
p
o
p
u
latio
n
.
As
t
h
e
s
ea
r
c
h
p
r
o
ce
ed
s
,
th
e
ev
o
l
u
tio
n
ar
y
o
p
er
ato
r
s
(
s
elec
tio
n
,
r
ec
o
m
b
in
at
io
n
an
d
m
u
ta
tio
n
)
b
r
in
g
ch
a
n
g
e
s
in
t
h
e
p
o
p
u
latio
n
b
y
ad
d
in
g
/d
ele
tin
g
/
m
o
d
i
f
y
in
g
t
h
e
ca
n
d
id
ates.
T
h
is
e
v
o
lu
tio
n
ar
y
ch
a
n
g
e
in
th
e
p
o
p
u
latio
n
s
p
ac
es
ca
n
b
e
d
ep
icted
as
a
n
a
d
ap
tiv
e
p
o
p
u
latio
n
lan
d
s
ca
p
e.
An
ex
a
m
p
le
ad
ap
tiv
e
lan
d
s
ca
p
e
o
f
a
p
o
p
u
latio
n
w
it
h
t
w
o
d
i
m
e
n
s
io
n
a
l
p
r
o
b
lem
s
is
s
h
o
w
n
in
Fig
u
r
e
2
(
T
h
is
f
ig
u
r
e
is
d
ir
ec
tl
y
tak
e
n
f
r
o
m
[
4
]
)
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esia
n
J
E
lec
E
n
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&
C
o
m
p
Sci
I
SS
N:
2502
-
4752
ļ²
Th
eo
r
etica
l A
n
a
lysi
s
a
n
d
E
mp
ir
ica
l Co
mp
a
r
is
o
n
o
f D
iffer
en
t
P
o
p
u
la
tio
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(
Dev
ika
.
K
)
89
Fig
u
r
e
1
.
T
h
e
s
tr
u
ctu
r
e
o
f
EA
Fi
g
u
r
e
2
.
A
s
a
m
p
le
lan
d
s
ca
p
e
o
f
a
p
o
p
u
latio
n
4.
P
O
P
UL
AT
I
O
N
I
N
I
T
I
AL
I
Z
AT
I
O
N
T
E
CH
N
I
Q
UE
S
T
h
e
p
o
p
u
latio
n
in
it
ializatio
n
is
th
e
in
i
tial
s
tep
f
o
r
all
EAs
an
d
it
r
an
d
o
m
l
y
o
r
h
eu
r
i
s
ticall
y
p
r
o
v
id
e
s
in
itial
g
u
e
s
s
o
f
s
o
lu
tio
n
s
.
I
f
t
h
e
i
n
itial
g
u
e
s
s
e
s
ar
e
g
o
o
d
,
it
h
elp
s
t
h
e
EA
to
f
i
n
d
t
h
e
o
p
ti
m
al
s
o
lu
t
io
n
f
a
s
ter
.
T
h
e
w
r
o
n
g
i
n
itial
g
u
es
s
es
w
i
l
l
af
f
ec
t
t
h
e
p
er
f
o
r
m
an
ce
th
e
EA
,
b
y
d
ir
ec
ti
n
g
it
to
w
a
s
te
t
h
e
ti
m
e
i
n
s
ea
r
ch
i
n
g
f
o
r
th
e
s
o
l
u
tio
n
s
i
n
th
e
n
o
n
-
p
r
o
m
is
i
n
g
ar
ea
o
f
t
h
e
s
o
lu
tio
n
s
p
ac
e.
A
l
s
o
,
it
i
s
d
i
f
f
icu
lt
to
d
et
er
m
in
e
w
h
eth
er
th
e
in
itial
ized
p
o
p
u
latio
n
is
g
o
o
d
o
r
n
o
t.
Sin
ce
s
u
cc
e
s
s
o
f
an
EA
s
tar
ts
w
i
th
a
g
o
o
d
in
itial
p
o
p
u
lat
io
n
,
in
v
es
tig
a
tin
g
a
n
d
p
r
o
p
o
s
in
g
d
if
f
er
en
t
p
o
p
u
latio
n
in
itial
izatio
n
tec
h
n
iq
u
e
s
is
a
g
o
o
d
r
esear
ch
ar
ea
f
o
r
th
e
EC
co
m
m
u
n
it
y
.
T
h
er
e
ar
e
m
a
n
y
p
o
p
u
lat
io
n
i
n
itial
izatio
n
(
PI
)
tech
n
iq
u
e
s
p
r
o
p
o
s
ed
b
y
d
i
f
f
er
en
t
r
esear
c
h
er
s
,
w
h
ic
h
ar
e
s
u
p
p
o
r
tin
g
E
A
s
i
n
f
i
n
d
in
g
th
e
o
p
ti
m
al
s
o
l
u
tio
n
with
les
s
co
m
p
u
tatio
n
al
co
s
t [
5
-
8
]
.
T
h
is
s
ec
tio
n
d
is
c
u
s
s
e
s
d
i
f
f
e
r
en
t
PI
tec
h
n
iq
u
es
co
m
m
o
n
l
y
u
s
ed
i
n
EA
d
esi
g
n
.
T
h
e
d
if
f
er
en
t
p
o
p
u
latio
n
i
n
itializat
io
n
tec
h
n
iq
u
e
s
ar
e:
P
s
eu
d
o
R
an
d
o
m
Nu
m
b
er
Gen
er
ato
r
s
(
P
R
N
Gs
)
,
C
h
ao
tic
Nu
m
b
er
Gen
er
ato
r
s
(
C
N
Gs
)
,
Q
u
a
s
i
R
an
d
o
m
Seq
u
en
ce
(
QR
S
)
,
Un
i
f
o
r
m
E
x
p
er
i
m
en
tal
De
s
ig
n
(
UE
D
)
,
So
b
o
l Set
(
S
B
L
)
,
Go
o
d
L
attice
P
o
in
t
(
GLP
)
,
R
an
d
o
m
Star
t
Q
u
asi
R
a
n
d
o
m
Se
q
u
en
ce
(
R
S
QR
S
)
,
Scr
am
b
led
Qu
a
s
i
R
a
n
d
o
m
Seq
u
en
ce
(
S
QR
S
)
,
Mix
ed
P
s
eu
d
o
R
a
n
d
o
m
Seq
u
e
n
ce
(
MP
R
S
)
,
Op
p
o
s
itio
n
al
B
ased
L
e
ar
n
in
g
(
OB
L
)
an
d
C
en
tr
o
id
Vo
r
o
n
o
i te
s
s
ella
tio
n
(
C
V
T
)
T
h
ese
PI
tech
n
iq
u
e
s
ca
n
b
e
ca
teg
o
r
ized
in
to
t
h
r
ee
g
r
o
u
p
s
,
b
ased
o
n
t
h
eir
c
h
ar
ac
ter
is
tic
s
a
n
d
t
h
e
w
a
y
it
w
o
r
k
s
f
o
r
g
e
n
er
atin
g
th
e
r
an
d
o
m
n
u
m
b
er
s
.
T
h
e
t
h
r
ee
b
r
o
ad
ca
teg
o
r
ies
ar
e:
Gr
o
u
p
1
:
ā
R
a
n
d
o
m
n
ess
ā
g
r
o
u
p
,
Gr
o
u
p
2
: ā
C
o
mp
o
s
itio
n
a
lity
ā
g
r
o
u
p
an
d
Gr
o
u
p
3
: ā
Gen
era
lityā
g
r
o
u
p
T
h
e
PI
tech
n
iq
u
es
i
n
w
h
ic
h
t
h
e
r
an
d
o
m
n
u
m
b
er
s
ar
e
u
n
i
f
o
r
m
l
y
d
i
s
tr
ib
u
ted
a
n
d
p
r
ed
ictio
n
s
o
f
t
h
e
f
u
tu
r
e
v
al
u
es
ar
e
n
o
t
p
o
s
s
i
b
le
ar
e
g
r
o
u
p
ed
u
n
d
er
ā
r
a
n
d
o
mn
ess
ā
g
r
o
u
p
.
T
h
e
ā
c
o
mp
o
s
itio
n
a
lityā
o
f
PI
tech
n
iq
u
es
d
ea
l
s
w
it
h
th
e
n
u
m
b
er
o
f
s
tep
s
u
s
ed
f
o
r
g
en
er
a
tin
g
th
e
p
o
p
u
latio
n
an
d
ā
g
en
era
lityā
d
ea
ls
w
it
h
th
e
u
s
a
g
e
o
f
PI
tech
n
iq
u
e
to
s
o
lv
e
n
o
r
m
a
l
p
r
o
b
lem
s
as
w
ell
as
t
o
s
o
lv
e
s
p
ec
if
ic
p
r
o
b
lem
s
.
E
ac
h
g
r
o
u
p
h
as
ag
ai
n
t
w
o
s
u
b
ca
teg
o
r
ies.
A
c
h
ar
t
v
i
s
u
aliz
in
g
t
h
i
s
ca
te
g
o
r
izatio
n
is
s
h
o
w
n
in
Fi
g
u
r
e
3
,
an
d
t
h
e
s
u
b
ca
teg
o
r
ies ar
e
[
5
]
S
to
ch
a
s
tic
-
P
o
p
u
latio
n
d
ep
en
d
s
u
p
o
n
th
e
i
n
itia
l seed
,
Dete
r
mi
n
is
tic
-
A
l
w
a
y
s
g
en
er
ate
s
a
m
e
p
o
p
u
latio
n
,
N
o
n
-
co
mp
o
s
itio
n
a
l
:
-
P
r
o
d
u
ce
s
p
o
p
u
latio
n
i
n
a
s
i
n
g
le
s
tep
,
C
o
mp
o
s
itio
n
a
l
:
-
C
o
m
p
r
is
e
s
m
o
r
e
t
h
a
n
o
n
e
s
tep
,
Gen
eric
:
-
C
an
b
e
u
s
ed
i
n
all
t
y
p
e
o
f
o
p
ti
m
izatio
n
p
r
o
b
le
m
s
a
n
d
A
p
p
lica
tio
n
s
p
ec
ific
:
-
A
p
p
licab
le
to
p
ar
ticu
lar
r
ea
l
w
o
r
ld
p
r
o
b
lem
s
.
Fig
u
r
e
3
.
C
ateg
o
r
izatio
n
o
f
P
I
tech
n
iq
u
es
Evaluation Warning : The document was created with Spire.PDF for Python.
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I
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:
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.
12
,
No
.
1
,
Octo
b
er
2
0
1
8
:
87
ā
94
90
4
.
1
.
P
s
eudo
Ra
nd
o
m
Nu
m
b
er
G
ener
a
t
o
rs (
P
R
NGs
)
As
co
m
p
u
ter
s
f
ail
s
in
p
r
o
d
u
ci
n
g
tr
u
e
r
an
d
o
m
n
u
m
b
er
s
,
p
s
eu
d
o
r
an
d
o
m
n
u
m
b
er
s
ar
e
u
s
ed
to
g
en
er
ate
r
an
d
o
m
n
u
m
b
er
s
.
P
R
N
Gs
w
ill
g
en
er
ate
n
u
m
b
er
s
w
h
ic
h
lo
o
k
lik
e
r
an
d
o
m
.
R
a
n
k
in
g
o
f
d
i
f
f
e
r
en
t
P
R
N
G
s
ca
n
b
e
d
o
n
e
b
ased
o
n
t
w
o
f
ac
to
r
s
ā
C
yc
le_
Time
an
d
E
q
u
id
is
tr
ib
u
tio
n
.
C
yc
le_
Time
is
t
h
e
s
m
al
lest
i
n
teg
er
th
at
th
e
P
R
N
G
r
ep
ea
ts
p
r
o
d
u
cin
g
f
r
o
m
t
h
e
p
r
ev
io
u
s
l
y
p
r
o
d
u
ce
d
n
u
m
b
er
s
a
n
d
eq
u
id
is
tr
ib
u
tio
n
is
t
h
e
r
a
n
g
e
o
f
p
o
in
ts
w
h
ic
h
h
av
e
eq
u
al
d
i
s
tr
ib
u
tio
n
[
5
]
.
T
h
e
d
i
f
f
er
e
n
t
p
s
e
u
d
o
R
an
d
o
m
g
e
n
er
ato
r
s
ar
e:
(
1
)
GC
C
R
A
N
D
(
2
)
Mu
l
tip
l
y
W
i
th
C
ar
r
y
Ge
n
er
ato
r
(
M
W
C
)
(
3
)
C
o
m
p
li
m
e
n
tar
y
Mu
ltip
l
y
W
it
h
C
ar
r
y
Ge
n
er
at
o
r
(
C
MW
C
)
(
4
)
L
in
ea
r
C
o
n
g
r
u
en
t
ial
Gen
er
ato
r
(
LC
G
)
(
5
)
XOR
S
h
if
t
Gen
er
ato
r
(
X
OR
)
(
6
)
Me
r
s
en
n
e
T
w
is
ter
(
MT
)
(
7
)
W
el
l
E
q
u
id
is
tr
ib
u
ted
L
o
n
g
P
er
io
d
L
in
ea
r
(
W
E
LL
)
(
8
)
Kee
p
I
t
Si
m
p
l
e
Stu
p
id
(
K
I
S
S
).
T
h
e
GC
C
R
A
N
D
is
a
n
i
n
b
u
il
t
P
R
N
G
w
h
ic
h
i
s
a
v
ailab
le
in
a
ll
p
r
o
g
r
a
m
m
i
n
g
la
n
g
u
a
g
es
li
k
e
C
,
C
++
.
T
h
e
MWC
ca
n
b
e
u
s
ed
to
g
e
n
er
ate
r
an
d
o
m
n
u
m
b
er
s
q
u
ic
k
l
y
an
d
s
lig
h
t
m
o
d
if
icat
io
n
i
n
m
o
d
u
lo
ar
ith
m
etic
g
iv
e
r
is
e
to
an
o
t
h
er
P
R
N
G
ca
l
led
C
M
WC
.
T
h
e
X
OR
P
R
N
G
u
s
es
E
xc
lu
s
ive
-
OR
B
o
o
lean
o
p
er
atio
n
to
g
en
er
ate
r
an
d
o
m
n
u
m
b
er
s
.
O
n
e
o
f
th
e
w
e
ll
-
k
n
o
w
n
a
n
d
t
h
e
o
ld
est
P
R
N
G
is
LC
G
an
d
i
t
g
e
n
er
at
es
n
o
n
co
n
ti
n
u
o
u
s
r
an
d
o
m
n
u
m
b
er
s
u
s
i
n
g
li
n
ea
r
eq
u
atio
n
s
.
T
o
g
en
er
ate
h
i
g
h
q
u
alit
y
r
a
n
d
o
m
n
u
m
b
er
s
MT
is
u
s
ed
an
d
itās
th
e
m
o
s
t
w
id
el
y
u
s
ed
P
R
N
G
.
Me
r
s
en
n
e
T
w
i
s
ter
n
a
m
e
i
s
d
er
iv
ed
f
r
o
m
Me
r
s
e
n
n
e
P
r
i
m
e
b
ec
a
u
s
e
th
e
p
er
io
d
len
g
th
ch
o
s
en
to
b
e
a
Me
r
s
e
n
n
e
P
r
im
e.
Me
r
s
e
n
n
e
P
r
i
m
e
is
a
p
r
i
m
e
n
u
m
b
er
th
at
is
o
n
e
les
s
t
h
an
p
o
w
er
o
f
t
w
o
.
T
h
e
MT
i
s
t
h
e
f
ir
s
t
P
R
N
G
to
g
e
n
er
ate
f
ast
an
d
h
i
g
h
q
u
ali
t
y
r
an
d
o
m
n
u
m
b
er
s
.
C
o
m
m
o
n
l
y
u
s
ed
v
er
s
io
n
o
f
MT
alg
o
r
ith
m
is
b
ased
o
n
Me
r
s
e
n
n
e
P
r
i
m
e
2
19937
-
1
.
Dif
f
er
en
t
v
er
s
io
n
s
o
f
W
E
L
L
g
e
n
er
ato
r
ar
e
p
r
o
p
o
s
ed
f
o
r
g
en
er
ati
n
g
r
an
d
o
m
n
u
m
b
er
s
.
T
h
e
K
I
S
S
P
R
N
G
ca
n
ā
t
b
e
u
s
ed
in
co
n
te
x
t
w
h
er
e
cr
y
p
t
o
g
r
ap
h
ic
s
ec
u
r
it
y
is
i
m
p
o
r
tan
t.
T
h
e
MT
is
a
v
aila
b
le
in
all
p
r
o
g
r
a
m
m
i
n
g
la
n
g
u
ag
es
s
o
itās
t
h
e
m
o
s
t
p
o
p
u
lar
m
et
h
o
d
in
P
R
N
G
.
C
h
o
ice
o
f
P
R
N
G
ca
n
a
f
f
ec
t
th
e
p
er
f
o
r
m
an
ce
o
f
EA
[
5
,
9
,
1
0
]
.
T
h
e
P
R
N
G
h
as
s
ev
er
al
m
er
its
a
n
d
d
em
er
it
s
.
T
h
e
m
er
it
s
ar
e
:
a.
P
R
NGā
s
ar
e
ea
s
i
l
y
a
v
ailab
le
i
n
ev
er
y
p
r
o
g
r
a
m
m
in
g
la
n
g
u
a
g
e
.
b.
T
h
er
e
is
n
o
r
estrictio
n
f
o
r
p
o
p
u
latio
n
s
ize
an
d
o
n
t
h
e
n
u
m
b
e
r
o
f
d
ec
is
io
n
v
ar
iab
les.
c.
Si
m
p
le
tec
h
n
iq
u
e.
d.
Un
i
f
o
r
m
p
o
p
u
latio
n
ca
n
b
e
g
e
n
er
ated
e.
T
r
an
s
f
o
r
m
atio
n
f
r
o
m
u
n
if
o
r
m
p
o
p
u
latio
n
to
b
iased
p
o
p
u
latio
n
is
ea
s
y
.
T
h
e
d
em
er
its
ar
e
:
a.
C
an
ā
t
g
en
er
ate
p
er
f
ec
t e
v
en
l
y
d
is
tr
ib
u
ted
p
o
in
ts
.
b.
P
R
NG
s
u
f
f
er
s
f
r
o
m
th
e
c
u
r
s
e
o
f
d
i
m
e
n
s
io
n
a
lit
y
.
T
h
ese
d
em
er
it
s
w
il
l
af
f
ec
t
E
A
p
r
o
ce
s
s
m
o
r
e
w
h
e
n
th
e
s
ea
r
ch
s
p
ac
e
is
v
ast
a
n
d
t
h
e
d
i
m
e
n
s
io
n
alit
y
o
f
th
e
p
r
o
b
lem
i
s
to
o
lo
w
.
4
.
2
.
Cha
o
t
ic
Nu
m
ber
G
e
ner
a
t
o
rs (
C
NGs
)
T
h
e
w
o
r
k
i
n
g
o
f
C
N
Gs
i
s
b
as
ed
o
n
ch
ao
s
th
eo
r
y
.
C
h
ao
s
is
v
er
y
s
e
n
s
iti
v
e
to
in
it
ial
co
n
d
it
io
n
s
.
I
t
is
d
if
f
ic
u
lt
to
p
r
ed
ict
th
e
n
u
m
b
er
s
g
e
n
er
ated
b
y
C
N
Gs
.
T
h
e
C
N
Gs
ma
i
n
l
y
u
s
e
r
ec
u
r
s
iv
e
al
g
o
r
ith
m
s
.
T
o
g
en
er
ate
ch
ao
tic
s
eq
u
en
ce
a
n
i
n
itial
s
ee
d
is
s
e
lecte
d
r
an
d
o
m
l
y
an
d
a
f
u
n
ctio
n
(
m
ap
)
is
ap
p
lied
o
n
it.
T
h
e
m
ap
is
ap
p
lied
s
ev
er
al
ti
m
e
s
to
t
h
e
p
r
ev
io
u
s
l
y
g
e
n
er
ated
n
u
m
b
er
s
to
g
et
t
h
e
s
eq
u
en
ce
.
Di
f
f
er
en
t
t
y
p
e
s
o
f
o
n
e
d
i
m
en
s
i
o
n
al
a
n
d
t
w
o
d
i
m
en
s
i
o
n
al
m
ap
s
ar
e
av
ailab
le
[
5
,
1
1
]
.
T
h
ey
ar
e
C
ir
cle
Ma
p
,
C
u
b
ic
Ma
p
,
Gau
s
s
Ma
p
,
I
C
MI
C
m
ap
,
L
o
g
i
s
tic
Ma
p
,
Si
n
u
s
o
id
al
I
ter
ato
r
,
T
en
t M
ap
,
B
ak
er
ā
s
Ma
p
,
A
r
n
o
ld
ā
s
Ma
p
an
d
Z
asla
v
s
k
li
ā
s
Ma
p
B
ak
er
ā
s
,
A
r
n
o
ld
ā
s
a
n
d
Z
a
s
la
v
s
k
li
ā
s
ar
e
t
w
o
d
i
m
e
n
s
io
n
al
m
ap
s
an
d
all
o
th
er
s
ar
e
o
n
e
d
i
m
en
s
io
n
a
l
m
ap
s
.
T
o
g
en
er
ate
a
p
o
p
u
lati
o
n
u
s
i
n
g
C
N
G
p
r
o
p
er
m
ap
s
a
r
e
r
eq
u
ir
ed
.
T
en
t
m
ap
is
m
o
s
t
co
m
m
o
n
l
y
u
s
ed
i
n
CNG
,
b
ec
a
u
s
e
it
h
a
s
h
i
g
h
er
it
er
ativ
e
s
p
ee
d
co
m
p
ar
ed
to
o
th
er
m
ap
s
.
T
en
t
m
ap
g
en
er
ate
u
n
i
f
o
r
m
l
y
g
e
n
er
ated
ch
ao
tic
s
eq
u
e
n
ce
w
it
h
i
n
th
e
r
a
n
g
e
o
f
[
0
,
1
]
.
T
h
e
m
ai
n
p
r
o
p
er
ties
o
f
C
N
G
a
r
e
er
g
o
d
icit
y
,
r
an
d
o
m
n
ess
a
n
d
r
eg
u
lar
it
y
.
Op
ti
n
g
CNG
a
s
PI
tech
n
iq
u
e
w
il
l i
m
p
r
o
v
e
t
h
e
p
er
f
o
r
m
a
n
ce
o
f
EA
in
ter
m
s
o
f
p
o
p
u
latio
n
s
ize,
s
u
cc
es
s
r
ate
an
d
co
n
v
er
g
e
n
ce
r
ate
[
5
,
1
1
]
.
4
.
3
.
Q
ua
s
i R
a
nd
o
m
Se
qu
e
nc
e
(
QR
S
)
T
h
e
QR
S
w
ill
g
e
n
er
ate
t
h
e
s
eq
u
en
ce
s
w
h
ic
h
ar
e
n
ei
th
er
t
r
u
e
r
an
d
o
m
n
o
r
p
s
e
u
d
o
r
an
d
o
m
a
n
d
it
d
o
esn
ā
t
r
eq
u
ir
e
an
y
r
an
d
o
m
e
le
m
e
n
t.
T
h
e
QR
S
is
also
ca
ll
ed
as
lo
w
d
is
cr
ep
an
c
y
s
eq
u
en
ce
s
o
r
s
u
b
r
an
d
o
m
s
eq
u
en
ce
s
.
I
n
w
o
r
s
t
ca
s
e
QR
S
ca
n
b
e
n
o
n
-
u
n
if
o
r
m
.
C
o
m
p
ar
ed
to
P
R
N
G,
th
e
QR
S
i
s
u
s
ed
i
n
h
i
g
h
d
i
m
e
n
s
io
n
a
l
p
r
o
b
lem
s
.
So
m
eti
m
es t
h
e
n
u
m
er
ical
alg
o
r
ith
m
s
i
n
t
h
e
QR
S
w
il
l c
o
n
tr
ad
ict
ea
ch
o
th
er
[
5
]
.
4
.
4
.
Unifo
rm
E
x
peri
m
e
nta
l D
esig
n (
UE
D
)
I
t
is
a
t
y
p
e
o
f
s
p
ac
e
f
il
lin
g
al
g
o
r
ith
m
w
h
ic
h
lo
o
k
s
f
o
r
th
e
p
o
in
ts
th
at
h
a
v
e
to
b
e
ev
en
l
y
d
is
tr
ib
u
ted
in
a
g
i
v
en
r
a
n
g
e.
T
h
e
UE
D
i
s
m
ain
l
y
u
s
ed
in
co
m
p
u
ter
s
i
m
u
l
ated
d
esig
n
s
.
T
h
e
QR
S
u
s
e
s
o
n
l
y
o
n
e
d
i
m
en
s
io
n
p
r
o
j
ec
tio
n
w
h
er
ea
s
UE
D
u
s
es
D
d
i
m
en
s
io
n
p
r
o
j
ec
tio
n
s
th
i
s
i
s
o
n
e
ad
v
an
tag
e
o
f
UE
D
o
v
er
QR
S
[
5
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
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n
d
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n
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&
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Dev
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.
K
)
91
4
.
5
.
So
bo
l Set
(
SB
L
)
T
h
e
S
B
L
w
ill
g
en
er
ate
p
o
p
u
l
atio
n
s
w
h
ich
ar
e
w
ell
d
is
tr
ib
u
ted
in
th
e
d
ec
is
io
n
s
p
ac
e.
T
h
e
So
b
o
l
s
eq
u
en
ce
v
al
u
es
w
i
ll
b
e
in
b
et
w
ee
n
ze
r
o
an
d
o
n
e.
Mo
s
t
co
m
m
o
n
l
y
u
s
ed
al
g
o
r
ith
m
f
o
r
g
en
er
ati
n
g
s
o
b
o
l
s
eq
u
en
ce
i
s
A
lg
o
r
ith
m
-
659
an
d
it c
an
g
en
er
ate
t
h
e
i
n
teg
er
s
u
p
to
4
0
d
im
e
n
s
io
n
s
[
8
]
.
4
.
6
.
G
o
o
d L
a
t
t
ice
P
o
int
(
GL
P
)
T
h
e
GLP
g
e
n
er
ates
p
o
in
ts
w
h
i
ch
ar
e
e
v
en
l
y
d
is
tr
ib
u
te
s
i
n
th
e
d
ec
is
io
n
s
p
ac
e.
L
attice
p
o
i
n
t i
s
a
g
r
o
u
p
o
f
p
o
in
ts
i
n
th
e
s
a
m
e
lo
ca
tio
n
.
L
attice
r
u
les ar
e
u
s
ed
to
cr
ea
te
s
eq
u
en
ce
i
n
GLP
[
8
]
.
4
.
7
.
Cent
ro
id Vo
r
o
no
i Tes
s
e
lla
t
io
n (
CVT
)
T
h
e
C
V
T
h
elp
s
to
d
iv
id
e
th
e
s
ea
r
ch
s
p
ac
e
in
eq
u
al
v
o
lu
m
es.
T
h
e
C
V
T
d
o
esn
ā
t
u
s
e
f
i
tn
e
s
s
f
u
n
ct
io
n
to
ev
alu
a
te
th
e
p
o
p
u
latio
n
.
I
n
itia
l
p
o
p
u
latio
n
is
cr
ea
ted
u
s
in
g
a
n
y
o
f
th
e
PI
tec
h
n
iq
u
es.
T
h
e
p
o
p
u
latio
n
s
p
ac
e
is
d
iv
id
ed
in
to
s
o
m
e
p
ar
titi
o
n
s
u
s
i
n
g
r
an
d
o
m
l
y
g
e
n
er
ated
au
x
i
liar
y
p
o
i
n
ts
.
T
h
ese
p
ar
tit
io
n
s
ar
e
iter
ati
v
el
y
en
h
a
n
ce
d
till
t
h
e
ter
m
i
n
atio
n
c
r
iter
ia
ar
e
m
et
[
5
]
.
4
.
8
.
O
pp
o
s
it
io
na
l Ba
s
ed
L
ea
rning
(
OB
L
)
I
n
itiall
y
,
OB
L
g
e
n
er
ates
p
o
p
u
latio
n
ca
lled
o
r
ig
i
n
al
p
o
p
u
l
atio
n
.
T
h
e
o
r
ig
in
al
p
o
p
u
lat
io
n
ca
n
b
e
g
en
er
ated
u
s
i
n
g
a
n
y
o
f
t
h
e
e
x
i
s
tin
g
p
o
p
u
latio
n
i
n
itia
lizatio
n
tech
n
iq
u
es.
T
h
e
n
a
n
e
w
p
o
p
u
l
atio
n
is
cr
ea
ted
b
y
ap
p
ly
i
n
g
s
o
m
e
h
e
u
r
is
tic
s
r
u
l
es
an
d
th
a
t
n
e
w
p
o
p
u
latio
n
i
s
ca
lled
o
p
p
o
s
ite
p
o
p
u
latio
n
.
B
y
co
m
p
ar
i
n
g
th
e
f
it
n
es
s
o
f
th
e
ca
n
d
id
ates
in
b
o
th
th
e
p
o
p
u
latio
n
s
,
a
s
u
b
s
et
is
cr
ea
ted
f
r
o
m
th
e
u
n
io
n
o
f
i
n
itial
a
n
d
o
p
p
o
s
ite
p
o
p
u
latio
n
s
.
Ma
in
g
o
al
o
f
OB
L
is
to
m
a
k
e
th
e
p
o
p
u
latio
n
cl
o
s
er
to
th
e
o
p
tim
a
l
s
o
lu
t
io
n
[
5
,
8
]
.
T
h
e
v
ar
ian
ts
o
f
OB
L
ar
e
Qu
a
s
i
Op
p
o
s
itio
n
B
ased
L
ea
r
n
in
g
(
QOB
L
)
,
Qu
a
s
i
R
ef
lec
tio
n
Op
p
o
s
iti
o
n
B
ased
L
ea
r
n
i
n
g
(
QR
OB
L
)
,
C
en
ter
B
ased
Sa
m
p
li
n
g
(
C
B
S
)
,
Gen
er
alize
d
Op
p
o
s
itio
n
B
ased
L
ea
r
n
in
g
(
GOB
L
)
an
d
C
u
r
r
en
t
Op
ti
m
u
m
Op
p
o
s
itio
n
B
ased
L
ea
r
n
i
n
g
(
C
OOB
L
)
I
n
QOB
L
i
n
s
tead
o
f
ac
t
u
al
o
p
p
o
s
ite
p
o
in
t
q
u
as
i
o
p
p
o
s
ite
p
o
in
t
is
u
s
ed
.
Qu
a
s
i
o
p
p
o
s
ite
p
o
in
t
i
s
a
p
o
in
t
w
h
ic
h
i
s
r
an
d
o
m
l
y
g
en
er
ated
an
d
itā
s
lo
ca
ted
b
et
w
e
en
th
e
o
p
p
o
s
ite
p
o
in
t
a
n
d
th
e
m
id
d
le
p
o
in
t.
T
h
e
p
er
f
o
r
m
a
n
ce
o
f
OB
L
a
n
d
its
v
ar
ian
ts
d
ep
en
d
s
u
p
o
n
t
h
e
o
r
ig
i
n
al
p
o
p
u
latio
n
.
C
o
m
p
ar
ed
to
OB
L
an
d
QOB
L
th
e
QR
OB
L
w
ill
g
en
er
ate
t
h
e
p
o
p
u
latio
n
w
h
ic
h
is
m
o
r
e
clo
s
e
to
th
e
o
p
ti
m
al
s
o
l
u
tio
n
.
T
h
e
OB
L
ten
d
s
to
f
in
d
t
h
e
o
p
tim
a
l
s
o
l
u
tio
n
f
a
s
ter
th
a
n
o
th
er
PI
tech
n
iq
u
e
s
.
Se
v
er
al
s
t
u
d
ies
clai
m
t
h
at
f
o
r
w
id
e
r
an
g
e
o
f
p
r
o
b
le
m
s
th
e
b
est
p
er
f
o
r
m
in
g
PI
tech
n
iq
u
e
is
OB
L
[
1
0
]
.
T
h
e
CNG
,
OB
L
an
d
QOB
L
PI
tech
n
iq
u
es
ca
n
w
o
r
k
w
el
l
o
n
b
o
th
h
ig
h
d
i
m
e
n
s
io
n
al
a
n
d
lo
w
d
i
m
en
s
io
n
a
l
p
r
o
b
le
m
s
.
Stu
d
ie
s
s
h
o
w
t
h
at
PI
co
m
p
o
n
e
n
t
o
f
EA
is
a
n
i
n
ter
est
in
g
r
esear
ch
s
e
g
m
en
t
f
o
r
EC
co
m
m
u
n
it
y
.
Fu
r
t
h
er
r
esear
ch
s
t
u
d
i
es
o
n
t
h
i
s
ca
n
ad
d
n
e
w
ad
v
a
n
c
ed
PI
tech
n
iq
u
es
to
w
o
r
k
w
el
l o
n
lar
g
e
s
ca
le
o
p
tim
izatio
n
p
r
o
b
l
em
s
.
As
d
is
c
u
s
s
ed
ab
o
v
e,
t
h
er
e
ex
is
t
s
e
v
er
al
PI
tech
n
iq
u
es
f
o
r
EA
.
T
h
e
p
er
f
o
r
m
an
ce
o
f
t
h
e
EA
ca
n
b
e
af
f
ec
ted
b
y
t
h
e
u
s
a
g
e
o
f
p
ar
ticu
lar
PI
tech
n
iq
u
e.
C
ate
g
o
r
izat
io
n
o
f
PI
tec
h
n
iq
u
e
s
g
iv
e
s
a
r
o
u
g
h
p
ict
u
r
e
ab
o
u
t
th
e
w
o
r
k
in
g
o
f
v
ar
io
u
s
PI
tec
h
n
iq
u
es.
E
ac
h
tec
h
n
iq
u
e
h
as
d
if
f
er
e
n
t
v
ar
ian
ts
.
T
h
e
PI
is
t
h
e
i
n
itial
s
ta
g
e
o
f
all
EA
,
th
er
ef
o
r
e
it is
i
m
p
o
r
tan
t to
ch
o
o
s
e
th
e
b
est
s
u
itab
le
PI
tech
n
iq
u
e
f
o
r
th
e
p
r
o
b
le
m
to
b
e
s
o
lv
ed
.
T
h
e
s
tu
d
ie
s
o
n
PI
tec
h
n
iq
u
es
r
ev
ea
l
th
at
OB
L
is
t
h
e
b
est
PI
tech
n
iq
u
e
f
o
r
EA
[
1
2
,
1
3
]
w
h
i
c
h
h
elp
s
in
g
e
n
er
ati
n
g
h
i
g
h
q
u
alit
y
s
o
lu
t
io
n
s
.
T
h
e
OB
L
b
ased
PI
w
as e
x
p
er
i
m
e
n
ted
o
n
DE
alg
o
r
ith
m
in
[
1
4
]
.
5.
DE
S
I
G
N
O
F
E
XP
E
R
I
M
E
NT
T
h
e
DE
alg
o
r
ith
m
p
r
o
p
o
s
ed
b
y
R
ai
n
er
Sto
r
n
i
n
[
1
5
]
is
u
s
ed
i
n
t
h
is
e
x
p
er
i
m
e
n
t.
DE
h
as
t
h
e
alg
o
r
ith
m
ic
s
tr
u
ct
u
r
e
s
i
m
ilar
to
o
th
er
EAs
[
1
6
]
,
h
o
w
ev
e
r
it
h
as
a
u
n
iq
u
e
m
u
tatio
n
s
ch
e
m
e
ca
lled
as
d
iffer
en
tia
l
mu
ta
tio
n
.
T
h
er
e
ar
e
m
a
n
y
r
esear
ch
w
o
r
k
s
to
p
r
o
p
o
s
e
i
m
p
r
o
v
ed
DE
al
g
o
r
ith
m
s
[
1
7
,
1
8
]
an
d
to
u
s
e
it f
o
r
r
ea
l ti
m
e
o
p
ti
m
izatio
n
p
r
o
b
lem
s
[
1
9
]
.
T
h
er
e
ex
is
ts
f
e
w
w
o
r
k
s
w
h
er
e
d
if
f
er
en
t p
o
p
u
l
at
io
n
i
n
itializat
io
n
i
s
ex
p
er
i
m
e
n
ted
f
o
r
DE
[
2
0
,
2
1
]
.
A
r
esear
ch
w
o
r
k
to
s
tu
d
y
t
h
e
p
er
f
o
r
m
a
n
ce
o
f
OB
L
PI
tech
n
iq
u
e
u
s
i
n
g
co
n
v
er
g
e
n
ce
s
p
ee
d
is
p
r
esen
t
ed
in
[
1
2
]
,
f
o
r
a
DE
v
ar
ian
t
w
i
th
3
4
b
en
c
h
m
ar
k
f
u
n
ctio
n
s
.
T
h
e
p
o
p
u
latio
n
d
y
n
a
m
ics
o
f
t
h
e
c
h
o
s
e
n
DE
a
lg
o
r
ith
m
s
is
a
n
al
y
ze
d
w
ell
a
n
d
r
ep
o
r
ted
in
th
e
liter
at
u
r
e
u
s
in
g
th
e
r
an
d
o
m
PI
tech
n
iq
u
e
[
2
2
-
2
5
]
.
T
h
e
e
m
p
ir
ical
r
es
u
lt
s
o
b
tai
n
e
d
b
y
i
m
p
le
m
en
t
in
g
t
w
o
PI
t
ec
h
n
iq
u
es
f
o
r
DE
is
p
r
esen
t
ed
in
th
i
s
Sectio
n
.
T
h
e
DE
alg
o
r
ith
m
h
as
b
ee
n
i
m
p
le
m
e
n
ted
u
s
in
g
r
an
d
o
m
an
d
OB
L
PI
tech
n
iq
u
es
.
T
h
e
ex
p
er
im
e
n
ta
l
s
etu
p
in
cl
u
d
es
4
d
if
f
er
en
t
DE
v
ar
ia
n
ts
an
d
4
b
en
c
h
m
ar
k
in
g
f
u
n
c
tio
n
s
.
T
h
e
DE
v
ar
ian
t
s
u
s
ed
ar
e
DE
/r
a
n
d
/1
/b
in
,
DE
/r
a
n
d
/1
/ex
p
,
DE
/b
est/
1
/b
in
an
d
DE
/b
est/
1
/exp
.
T
h
e
b
en
ch
m
ar
k
f
u
n
ctio
n
s
u
s
ed
ar
e
Sp
h
er
e
m
o
d
el
ā
f
1
,
Sch
e
w
e
f
el
's
p
r
o
b
le
m
-
f
2
,
Ge
n
er
alize
d
R
o
s
e
n
b
r
o
ck
's
f
u
n
ctio
n
ā
f
3
an
d
Ge
n
er
alize
d
Sch
e
w
e
f
el
's
p
r
o
b
lem
ā
f
4
.
T
h
e
d
etails
o
f
th
e
b
en
ch
m
ar
k
in
g
f
u
n
ctio
n
s
ar
e
p
r
esen
ted
in
T
a
b
le
1
.
T
h
e
d
esig
n
ex
p
er
i
m
e
n
t
in
cl
u
d
es
s
ett
in
g
u
p
v
al
u
es
f
o
r
th
e
p
ar
a
m
eter
s
.
T
h
e
p
ar
am
eter
s
f
o
r
DE
a
lg
o
r
it
h
m
a
n
d
t
h
eir
co
r
r
esp
o
n
d
in
g
v
alu
e
s
u
s
ed
in
t
h
e
ex
p
er
i
m
en
t a
r
e
s
h
o
w
n
i
n
T
ab
le
2
.
Evaluation Warning : The document was created with Spire.PDF for Python.
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&
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m
p
Sci,
Vo
l.
12
,
No
.
1
,
Octo
b
er
2
0
1
8
:
87
ā
94
92
T
h
e
PI
tech
n
iq
u
e
s
u
s
ed
in
t
h
e
ex
p
er
i
m
e
n
t
ar
e
r
an
d
o
m
PI
a
n
d
OB
L
PI
.
T
h
e
DE
alg
o
r
ith
m
s
w
it
h
t
h
es
e
PI
tech
n
iq
u
es
ar
e
n
a
m
es
as
DE
R
P
I
an
d
DE
OBL
P
I
,
h
en
ce
f
o
r
th
.
T
h
e
y
ar
e
i
m
p
le
m
e
n
ted
f
o
r
t
w
o
d
i
f
f
er
e
n
t
p
o
p
u
latio
n
s
izes:
NP
=
5
(
w
i
th
D
=
5
)
a
n
d
NP
=
6
0
(
w
i
t
h
D
=
3
0
)
.
I
n
DE
R
P
I
,
t
h
e
v
al
u
es
f
o
r
ea
c
h
o
f
t
h
e
co
m
p
o
n
e
n
t
o
f
a
ch
r
o
m
o
s
o
m
e
ar
e
g
e
n
er
ated
r
an
d
o
m
l
y
w
it
h
i
n
t
h
e
allo
w
ed
r
an
g
e
m
en
tio
n
ed
i
n
th
e
b
en
ch
m
ar
k
i
n
g
f
u
n
ctio
n
s
.
Fo
r
th
e
ch
r
o
m
o
s
o
m
e
i
o
f
t
h
e
p
o
p
u
l
atio
n
X
,
th
e
j
th
co
m
p
o
n
e
n
t i
s
in
itialized
as f
o
llo
w
s
[
]
(
)
(
)
(
1
)
w
h
er
e
xl
an
d
xu
ar
e
th
e
lo
w
er
an
d
u
p
p
er
b
o
u
n
d
o
f
th
e
allo
wed
v
alu
es
o
f
th
e
co
m
p
o
n
en
t
an
d
s
ee
d
is
th
e
i
n
p
u
t
f
o
r
th
e
r
an
d
o
m
n
u
m
b
er
g
e
n
er
a
to
r
.
I
n
DE
OBL
P
I
,
a
n
i
n
it
ial
p
o
p
u
la
tio
n
is
cr
ea
ted
r
an
d
o
m
l
y
(
as
a
b
o
v
e)
th
e
n
a
n
o
p
p
o
s
ite
p
o
p
u
latio
n
is
g
en
er
ated
w
i
th
t
h
is
in
i
tial
p
o
p
u
latio
n
w
h
ich
co
n
tai
n
s
t
h
e
o
p
p
o
s
ite
o
f
ea
c
h
i
n
d
iv
id
u
al.
T
h
e
o
p
p
o
s
ite
ca
n
d
id
ate
(
OX
i
)
f
o
r
ea
ch
ca
n
d
id
ate
(
X
i
)
in
th
e
p
o
p
u
latio
n
i
s
cr
ea
ted
u
s
i
n
g
t
h
e
eq
u
atio
n
(
2
)
.
Ne
w
p
o
p
u
latio
n
is
cr
ea
ted
b
y
co
m
b
i
n
i
n
g
t
h
e
in
itial
p
o
p
u
lati
o
n
an
d
o
p
p
o
s
ite
p
o
p
u
latio
n
.
T
h
en
th
e
b
est
NP
ca
n
d
id
ates
f
r
o
m
th
e
co
m
b
i
n
ed
p
o
p
u
latio
n
ar
e
s
elec
ted
f
o
r
in
it
ial
p
o
p
u
latio
n
.
[
]
[
]
(
2
)
T
h
e
p
er
f
o
r
m
an
ce
o
f
DE
R
P
I
a
n
d
DE
OBLP
I
is
co
m
p
ar
ed
b
y
t
h
e
m
ea
n
o
b
j
ec
tiv
e
f
u
n
ctio
n
(
MOV
)
v
alu
es.
T
h
e
MOV
is
t
h
e
av
er
a
g
e
o
f
t
h
e
b
est
o
b
j
ec
tiv
e
f
u
n
ctio
n
v
al
u
es
o
b
tain
ed
b
y
th
e
a
lg
o
r
it
h
m
at
th
e
e
n
d
o
f
ea
c
h
r
u
n
.
I
t
is
ca
lc
u
lated
as
f
o
llo
w
s
(
ā
)
(
3
)
w
h
er
e
Ma
xR
u
n
is
th
e
m
a
x
i
m
u
m
n
u
m
b
er
o
f
r
u
n
s
(
w
h
ich
i
s
s
et
as
5
0
)
an
d
B
est
OV
i
is
th
e
b
est
o
b
j
ec
tiv
e
f
u
n
ctio
n
v
al
u
e
o
b
tain
ed
b
y
t
h
e
alg
o
r
ith
m
f
o
r
t
h
e
r
u
n
i
.
T
ab
le
1
.
Fu
n
ctio
n
al
d
escr
ip
tio
n
o
f
t
h
e
b
en
ch
m
ar
k
in
g
f
u
n
ctio
n
s
f
1
ā
S
p
h
e
r
e
m
o
d
e
l
(
)
ā
(
)
;
(
)
f
2
ā
S
c
h
w
e
f
e
l
ā
s
Pr
o
b
l
e
m
1
.
2
(
)
ā
(
ā
)
(
)
;
(
)
f
3
-
Gene
r
a
l
i
z
e
d
R
o
senb
r
o
c
k
'
s F
u
n
c
t
i
o
n
(
)
ā
|
(
)
(
)
|
(
)
;
(
)
f
4
ā
G
e
n
e
r
a
l
i
z
e
d
S
c
h
w
e
f
e
l
ā
s
Pr
o
b
l
e
m
2
.
2
6
(
)
ā
(
(
ā
|
|
)
)
(
)
;
(
)
T
ab
le
2
.
T
h
e
p
ar
a
m
eter
s
et
u
p
f
o
r
th
e
ex
p
er
i
m
en
t
Sno
Pa
r
a
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CO
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8
,
2
0
1
2
.
[2
]
S
h
u
f
a
n
g
W
u
,
T
ie
x
io
n
g
S
u
,
ā
Op
ti
m
iz
a
ti
o
n
De
sig
n
o
f
Ca
n
ti
lev
e
r
Be
a
m
f
o
r
Ca
n
ti
lev
e
r
Cra
n
e
Ba
s
e
d
o
n
Im
p
ro
v
e
d
GA
ā
,
In
d
o
n
e
sia
n
J
o
u
rn
a
l
o
f
El
e
c
trica
l
En
g
i
n
e
e
rin
g
a
n
d
C
o
mp
u
ter
S
c
ien
c
e
,
V
o
l
.
1
2
,
No
.
4
,
p
p
.
2
6
5
2
-
2
6
5
7
,
2
0
1
4
.
[3
]
L
iu
X
iao
x
i
o
n
g
,
W
a
n
g
Ju
a
n
,
W
u
y
a
n
,
L
iu
Yu
,
ā
T
h
e
Op
ti
m
iza
ti
o
n
o
f
L
a
ter
a
l
Co
n
tr
o
l
A
u
g
m
e
n
tatio
n
b
a
se
d
o
n
G
e
n
e
ti
c
A
l
g
o
rit
h
m
sā
.
In
d
o
n
e
sia
n
J
o
u
rn
a
l
o
f
El
e
c
trica
l
En
g
in
e
e
rin
g
a
n
d
Co
mp
u
ter
S
c
ien
c
e
,
V
o
l.
1
1
,
No
.
6
,
p
p
.
2
9
6
2
ā
2
9
6
7
,
2
0
1
3
.
[4
]
Ei
b
e
n
,
A
g
o
sto
n
E.
,
a
n
d
Ja
m
e
s
E.
S
m
it
h
.
In
tro
d
u
c
ti
o
n
t
o
e
v
o
lu
t
io
n
a
ry
c
o
mp
u
ti
n
g
.
Vo
l.
5
3
.
He
id
e
lb
e
rg
:
sp
rin
g
e
r,
2
0
0
3
.
[5
]
Ka
z
i
m
ip
o
u
r
B
o
rh
a
n
,
X
iao
d
o
n
g
L
i,
a
n
d
A
.
Ka
i
Qin
.
"
A
re
v
iew
o
f
p
o
p
u
lat
i
o
n
in
i
ti
a
li
z
a
ti
o
n
tec
h
n
i
q
u
e
s
f
o
r
e
v
o
lu
ti
o
n
a
ry
a
lg
o
rit
h
m
s."
2
0
1
4
I
EE
E
Co
n
g
re
ss
o
n
Evo
l
u
ti
o
n
a
ry
Co
mp
u
t
a
ti
o
n
(
CEC)
,
2
0
1
4
.
[6
]
Ko
n
d
a
m
a
d
u
g
u
la,
S
it
a
,
a
n
d
S
ri
n
a
t
h
R.
Na
i
d
u
.
"
A
c
c
e
lera
t
e
d
e
v
o
lu
ti
o
n
a
ry
a
l
g
o
rit
h
m
s
w
it
h
p
a
ra
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e
ter
i
m
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rtan
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e
b
a
se
d
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o
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u
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ti
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li
z
a
ti
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n
f
o
r
v
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riatio
n
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a
w
a
re
a
n
a
lo
g
y
ield
o
p
ti
m
iza
ti
o
n
.
"
Circ
u
it
s
a
n
d
S
y
ste
ms
(M
W
S
CAS
),
2
0
1
6
IEE
E
5
9
th
In
ter
n
a
ti
o
n
a
l
M
id
we
st
S
y
mp
o
si
u
m o
n
.
IEE
E,
2
0
1
6
.
[7
]
Ka
z
i
m
ip
o
u
r,
Bo
r
h
a
n
,
X
iao
d
o
n
g
L
i,
a
n
d
A
.
K
a
i
Qin
.
"
In
it
ializa
ti
o
n
m
e
th
o
d
s
f
o
r
larg
e
s
c
a
le
g
lo
b
a
l
o
p
ti
m
iza
ti
o
n
.
"
2
0
1
3
IE
EE
C
o
n
g
re
ss
o
n
Ev
o
lu
t
io
n
a
ry
Co
m
p
u
t
a
ti
o
n
(
CEC),
2
0
1
3
.
[8
]
Ka
z
i
m
ip
o
u
r
Bo
rh
a
n
,
X
iao
d
o
n
g
L
i,
a
n
d
A
.
K
a
i
Qin
.
"
E
ff
e
c
ts
o
f
p
o
p
u
latio
n
in
it
ializa
ti
o
n
o
n
d
if
f
e
r
e
n
ti
a
l
e
v
o
lu
ti
o
n
f
o
r
larg
e
sc
a
l
e
o
p
ti
m
iza
ti
o
n
.
"
2
0
1
4
I
EE
E
Co
n
g
re
ss
o
n
Evo
l
u
ti
o
n
a
ry
Co
mp
u
t
a
ti
o
n
(
CEC),
IEE
E,
2
0
1
4
.
Evaluation Warning : The document was created with Spire.PDF for Python.
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I
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N
:
2
5
0
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-
4752
I
n
d
o
n
esia
n
J
E
lec
E
n
g
&
C
o
m
p
Sci,
Vo
l.
12
,
No
.
1
,
Octo
b
er
2
0
1
8
:
87
ā
94
94
[9
]
Ra
jas
h
e
k
h
a
ra
n
,
Lek
sh
m
i,
a
n
d
C.
S
h
u
n
m
u
g
a
V
e
la
y
u
th
a
m
.
"
Is
Diff
e
r
e
n
ti
a
l
Ev
o
lu
ti
o
n
S
e
n
siti
v
e
to
P
se
u
d
o
Ra
n
d
o
m
Nu
m
b
e
r
G
e
n
e
ra
to
r
Qu
a
li
ty
?
ā
A
n
In
v
e
stig
a
ti
o
n
.
"
In
telli
g
e
n
t
S
y
ste
ms
T
e
c
h
n
o
lo
g
ies
a
n
d
Ap
p
li
c
a
t
io
n
s
.
S
p
rin
g
e
r,
C
h
a
m
,
2
0
1
6
.
3
0
5
-
3
1
3
.
[1
0
]
S
e
g
re
d
o
,
E
d
u
a
rd
o
,
e
t
a
l.
"
On
th
e
c
o
m
p
a
riso
n
o
f
in
it
ialisa
ti
o
n
st
ra
teg
ies
in
d
iff
e
re
n
ti
a
l
e
v
o
lu
ti
o
n
f
o
r
lar
g
e
sc
a
l
e
o
p
ti
m
isa
ti
o
n
.
"
Op
t
imiza
ti
o
n
L
e
tt
e
rs
,
p
p
.
1
-
1
4
,
2
0
1
7
.
[1
1
]
L
u
,
Hu
i,
e
t
a
l.
"
T
h
e
e
ff
e
c
ts
o
f
u
sin
g
Ch
a
o
ti
c
m
a
p
o
n
i
m
p
ro
v
in
g
th
e
p
e
rf
o
r
m
a
n
c
e
o
f
m
u
lt
io
b
jec
ti
v
e
e
v
o
lu
ti
o
n
a
ry
a
lg
o
rit
h
m
s."
M
a
th
e
ma
ti
c
a
l
Pro
b
l
e
ms
in
En
g
in
e
e
rin
g
2
0
1
4
(
2
0
1
4
).
[1
2
]
Ra
h
n
a
m
a
y
a
n
,
S
h
a
h
ry
a
r,
H
a
m
id
R.
T
izh
o
o
sh
,
a
n
d
M
a
g
d
y
M
A
S
a
la
m
a
.
"
A
n
o
v
e
l
p
o
p
u
latio
n
in
it
ializ
a
ti
o
n
m
e
th
o
d
f
o
r
a
c
c
e
ler
a
ti
n
g
e
v
o
lu
ti
o
n
a
ry
a
lg
o
rit
h
m
s."
Co
mp
u
ter
s
&
M
a
th
e
ma
ti
c
s
wit
h
Ap
p
l
ica
ti
o
n
s,
V
o
l
.
5
3
,
N
o
.
1
0
,
p
p
.
1
6
0
5
-
1
6
1
4
,
2
0
0
7
.
[1
3
]
S
h
a
h
ry
a
r
Ra
h
n
a
m
a
y
a
n
,
Ha
m
id
R.
T
izh
o
o
sh
,
a
n
d
M
a
g
d
y
M
.
A
.
S
a
la
m
a
,
ā
Op
p
o
siti
o
n
-
Ba
se
d
Dif
f
e
re
n
ti
a
l
Ev
o
lu
ti
o
n
ā
.
IEE
E
T
ra
n
sa
c
ti
o
n
s O
n
Evo
l
u
ti
o
n
a
ry
Co
mp
u
ta
t
io
n
,
V
o
l.
1
2
,
No
.
1
,
2
0
0
8
.
[1
4
]
Ra
h
n
a
m
a
y
a
n
S
.
,
T
izh
o
o
sh
H.R.
ā
Diffe
re
n
ti
a
l
Ev
o
lu
ti
o
n
V
ia
Ex
p
l
o
it
in
g
Op
p
o
site
P
o
p
u
lati
o
n
sā
.
In
:
T
izh
o
o
sh
H.R.
,
V
e
n
tres
c
a
M
.
(e
d
s)
Op
p
o
siti
o
n
a
l
Co
n
c
e
p
ts
in
C
o
mp
u
ta
t
io
n
a
l
In
te
ll
ig
e
n
c
e
,
S
tu
d
ies
in
Co
m
p
u
t
a
ti
o
n
a
l
In
telli
g
e
n
c
e
,
V
o
l
1
5
5
,
p
p
1
4
3
-
1
6
0
.
S
p
ri
n
g
e
r,
B
e
rli
n
,
He
id
e
l
b
e
rg
,
2
0
0
9
.
[1
5
]
S
to
rn
,
Ra
in
e
r,
a
n
d
Ke
n
n
e
th
P
rice
.
"
Diffe
re
n
ti
a
l
e
v
o
lu
ti
o
n
ā
a
sim
p
le
a
n
d
e
f
f
icie
n
t
h
e
u
risti
c
f
o
r
g
lo
b
a
l
o
p
ti
m
iza
ti
o
n
o
v
e
r
c
o
n
ti
n
u
o
u
s sp
a
c
e
s."
J
o
u
rn
a
l
o
f
g
lo
b
a
l
o
p
ti
miza
ti
o
n
,
V
o
l
.
1
1
,
N
o
.
4
,
p
p
.
3
4
1
-
3
5
9
,
1
9
9
7
.
[1
6
]
Jia
n
f
e
n
g
Qiu
,
Jiw
e
n
W
a
n
g
,
D
a
n
Ya
n
g
,
Ju
a
n
x
ie.
ā
A
Co
m
p
a
ris
o
n
o
f
I
m
p
ro
v
e
d
A
rti
f
icia
l
Be
e
Co
lo
n
y
A
lg
o
rit
h
m
s
Ba
se
d
o
n
Diff
e
re
n
ti
a
l
Ev
o
lu
ti
o
n
ā
,
In
d
o
n
e
sia
n
J
o
u
rn
a
l
o
f
E
lec
trica
l
En
g
i
n
e
e
rin
g
a
n
d
Co
mp
u
ter
S
c
ien
c
e
,
V
o
l.
1
1
,
No
.
1
0
,
p
p
.
5
5
7
9
ā
5
5
8
7
,
2
0
1
3
.
[1
7
]
L
in
g
ju
a
n
HO
U,
Zh
ij
ian
g
HO
U,
ā
A
No
v
e
l
Disc
r
e
te
Di
ff
e
re
n
ti
a
l
Ev
o
lu
ti
o
n
A
lg
o
rit
h
m
ā
,
In
d
o
n
e
sia
n
J
o
u
rn
a
l
o
f
El
e
c
trica
l
En
g
in
e
e
rin
g
a
n
d
Co
m
p
u
ter
S
c
ien
c
e
,
V
o
l
.
1
1
,
N
o
.
4
,
p
p
.
1
8
8
3
~
1
8
8
8
,
2
0
1
3
.
[1
8
]
Je
y
a
k
u
m
a
r,
G
.
a
n
d
S
h
u
n
m
u
g
a
V
e
la
y
u
th
a
m
,
C.
ā
Distrib
u
ted
He
tero
g
e
n
e
o
u
s
M
ix
in
g
o
f
Diff
e
re
n
ti
a
l
a
n
d
Dy
n
a
m
ic
Diff
e
r
e
n
ti
a
l
Ev
o
lu
ti
o
n
V
a
rian
ts
f
o
r
Un
c
o
n
stra
in
e
d
G
lo
b
a
l
O
p
ti
m
iza
ti
o
n
ā
,
S
o
ft
Co
m
p
u
ti
n
g
ā
S
p
rin
g
e
r
,
V
o
l
u
m
e
1
8
,
Iss
u
e
1
0
(
2
0
1
4
)
,
P
a
g
e
1
9
4
9
-
1
9
6
5
,
O
c
to
b
e
r
-
2
0
1
4
.
[1
9
]
Zain
Zah
a
r
n
,
R
u
if
e
n
g
S
h
i,
X
ia
n
g
ji
e
L
iu
,
ā
T
h
e
P
o
w
e
r
Un
it
Co
o
rd
i
n
a
ted
Co
n
tr
o
l
v
ia
Un
if
o
rm
Diff
e
r
e
n
ti
a
l
Ev
o
l
u
ti
o
n
A
l
g
o
rit
h
m
ā
.
In
d
o
n
e
sia
n
J
o
u
rn
a
l
o
f
El
e
c
trica
l
En
g
i
n
e
e
rin
g
a
n
d
Co
mp
u
ter
S
c
ien
c
e
,
V
o
l
.
1
1
,
No
.
7
,
p
p
.
3
4
9
8
-
3
5
0
7
,
2
0
1
3
.
[2
0
]
W
a
n
g
,
Jia
h
a
i,
W
e
i
w
e
i
Zh
a
n
g
,
a
n
d
Ju
n
Zh
a
n
g
.
"
Co
o
p
e
ra
ti
v
e
d
if
f
e
r
e
n
ti
a
l
e
v
o
lu
ti
o
n
w
it
h
m
u
lt
ip
le
p
o
p
u
latio
n
s
f
o
r
m
u
lt
io
b
jec
ti
v
e
o
p
ti
m
iza
ti
o
n
.
"
IEE
E
tra
n
s
a
c
ti
o
n
s o
n
c
y
b
e
rn
e
ti
c
s
4
6
.
1
2
(
2
0
1
6
):
2
8
4
8
-
2
8
6
1
.
[2
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2
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3
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,
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p
p
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1
7
0
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7
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2
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.
[2
5
]
Ra
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R
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a
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k
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to
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9
.
,
No
.
3
1
,
p
p
.
1
-
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0
,
2
0
1
6
.
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