I
nd
o
ne
s
ia
n J
o
urna
l o
f
E
lect
rica
l En
g
ineering
a
nd
Co
m
pu
t
er
Science
Vo
l.
3
7
,
No
.
2
,
Feb
r
u
ar
y
20
2
5
,
p
p
.
78
1
~
788
I
SS
N:
2
502
-
4
7
52
,
DOI
: 1
0
.
1
1
5
9
1
/ijee
cs
.v
3
7
.
i
2
.
pp
781
-
7
8
8
781
J
o
ur
na
l ho
m
ep
a
g
e
:
h
ttp
:
//ij
ee
cs
.
ia
esco
r
e.
co
m
Desig
ning
f
uzzy
memb
ership
func
tions
using
geneti
c alg
o
rithm
with
a
n
ew e
nco
di
ng
method
Ali
H
a
m
ed,
Sli
m
a
ne
H
irec
he
,
Abdelk
a
der
B
ek
ri
,
Ahm
ed
Cherie
t
La
b
o
r
a
t
o
r
y
o
f
C
O
A
S
E
E,
D
e
p
a
r
t
me
n
t
o
f
E
l
e
c
t
r
i
c
a
l
E
n
g
i
n
e
e
r
i
n
g
,
Ta
h
r
i
M
o
h
a
med
B
é
c
h
a
r
U
n
i
v
e
r
si
t
y
,
B
é
c
h
a
r
,
A
l
g
e
r
i
a
Art
icle
I
nfo
AB
S
T
RAC
T
A
r
ticle
his
to
r
y:
R
ec
eiv
ed
Sep
8
,
2
0
2
3
R
ev
is
ed
Sep
22
,
2
0
2
4
Acc
ep
ted
Sep
30
,
2
0
2
4
Th
is
a
rti
c
le
p
re
se
n
ts
a
n
e
w
m
e
th
o
d
f
o
r
d
e
sig
n
in
g
fu
z
z
y
m
e
m
b
e
rsh
ip
fu
n
c
ti
o
n
s
u
si
n
g
th
e
g
e
n
e
t
ic
a
lg
o
r
it
h
m
(
GA
)
with
o
u
t
t
h
e
u
se
o
f
c
o
n
stra
in
ts.
Co
n
v
e
n
ti
o
n
a
l
a
p
p
ro
a
c
h
e
s
to
d
e
sig
n
i
n
g
t
h
e
se
fu
n
c
ti
o
n
s
o
f
ten
i
n
v
o
l
v
e
m
a
n
u
a
l
tu
n
i
n
g
o
r
o
p
ti
m
iza
ti
o
n
tec
h
n
iq
u
e
s
with
li
m
it
a
ti
o
n
s
.
Ho
we
v
e
r,
t
h
is
a
rti
c
le
in
tro
d
u
c
e
s a
c
o
n
stra
i
n
t
-
fre
e
a
p
p
r
o
a
c
h
,
a
s th
e
GA
re
q
u
ires
a
ll
c
o
n
str
a
in
ts t
o
b
e
m
e
t
fo
r
a
c
h
r
o
m
o
so
m
e
;
if
e
v
e
n
o
n
e
c
o
n
d
it
io
n
is
n
o
t
sa
ti
sfie
d
,
th
e
c
h
ro
m
o
so
m
e
is
d
isc
a
rd
e
d
,
re
g
a
rd
les
s
o
f
it
s
i
d
e
a
l
v
a
lu
e
s
f
o
r
o
th
e
r
v
a
riab
les
.
Co
n
se
q
u
e
n
tl
y
,
a
h
i
g
h
n
u
m
b
e
r
o
f
c
o
n
stra
in
ts,
e
sp
e
c
ially
in
th
e
stu
d
ied
c
a
se
,
in
c
re
a
se
s
th
e
li
k
e
li
h
o
o
d
o
f
c
h
ro
m
o
so
m
e
re
jec
ti
o
n
,
lea
d
in
g
t
o
a
ti
m
e
-
c
o
n
su
m
in
g
d
e
sig
n
p
r
o
c
e
ss
a
n
d
su
b
o
p
ti
m
a
l
re
su
lt
s.
K
ey
w
o
r
d
s
:
C
o
n
s
tr
ain
t
-
f
r
ee
Fu
zz
y
lo
g
ic
Gen
etic
alg
o
r
ith
m
Me
m
b
er
s
h
ip
f
u
n
ctio
n
Op
tim
izatio
n
T
h
is i
s
a
n
o
p
e
n
a
c
c
e
ss
a
rticle
u
n
d
e
r th
e
CC B
Y
-
SA
li
c
e
n
se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
Ali
Ham
ed
L
ab
o
r
ato
r
y
of
C
OASE
E
,
Dep
ar
tm
en
t o
f
E
lectr
ical
E
n
g
in
ee
r
i
n
g
,
T
ah
r
i
Mo
h
am
e
d
B
éc
h
ar
U
n
iv
er
s
ity
B
éc
h
ar
,
Alg
er
ia
E
m
ail:
h
am
ed
.
ali.
in
f
@
g
m
ail.
c
o
m
1.
I
NT
RO
D
UCT
I
O
N
Fu
zz
y
lo
g
ic
c
o
n
tr
o
ller
s
(
FLCs
)
h
av
e
em
e
r
g
ed
as
in
d
is
p
en
s
ab
le
to
o
ls
f
o
r
ad
d
r
ess
in
g
co
m
p
lex
an
d
u
n
ce
r
tain
s
ce
n
ar
io
s
in
v
ar
io
u
s
f
ield
s
.
T
h
eir
ab
ilit
y
to
h
an
d
le
im
p
r
ec
is
e
d
ata
an
d
m
im
ic
h
u
m
an
d
ec
is
io
n
-
m
ak
in
g
p
r
o
ce
s
s
es
h
as
s
ig
n
if
ican
tly
co
n
tr
ib
u
ted
to
th
eir
wi
d
esp
r
ea
d
ad
o
p
tio
n
in
co
n
t
r
o
l
s
y
s
tem
s
[
1
]
.
Fro
m
en
g
in
ee
r
in
g
s
y
s
tem
s
to
f
in
an
cial
m
o
d
elin
g
,
FLCs
h
av
e
d
em
o
n
s
tr
ated
ad
ap
ta
b
ilit
y
an
d
ef
f
icac
y
,
o
f
f
er
in
g
ef
f
icien
t c
o
n
tr
o
l so
lu
tio
n
s
f
o
r
p
r
o
ce
s
s
es th
at
ch
allen
g
e
tr
ad
it
io
n
al
r
u
le
-
b
ased
ap
p
r
o
ac
h
es [
2
]
.
W
h
ile
th
e
d
em
an
d
f
o
r
o
p
tim
al
FLC
p
er
f
o
r
m
an
ce
co
n
tin
u
e
s
to
g
r
o
w,
v
ar
io
u
s
s
tr
ateg
ies
h
av
e
b
ee
n
ex
p
lo
r
ed
t
o
en
h
a
n
ce
th
eir
ef
f
icien
cy
.
On
e
s
u
ch
ap
p
r
o
ac
h
in
v
o
lv
es
lev
er
ag
in
g
th
e
ex
p
er
tis
e
o
f
d
o
m
ain
s
p
ec
ialis
t
s
to
cr
af
t
m
em
b
er
s
h
ip
f
u
n
ctio
n
s
an
d
r
u
le
s
ets
[
3
]
.
B
y
tap
p
in
g
in
to
th
eir
d
ee
p
u
n
d
er
s
tan
d
i
n
g
o
f
s
y
s
tem
b
eh
av
io
r
,
FLCs
ca
n
b
e
f
in
e
-
tu
n
ed
to
ac
h
iev
e
m
ax
i
m
u
m
e
f
f
ec
tiv
en
ess
.
Alter
n
ati
v
ely
,
c
o
m
p
u
tatio
n
al
in
tellig
en
ce
m
eth
o
d
s
lik
e
g
en
etic
alg
o
r
ith
m
s
(
GAs)
in
tr
o
d
u
ce
a
d
ata
-
d
r
i
v
en
d
im
e
n
s
io
n
to
o
p
tim
izatio
n
.
GAs
em
u
lates
n
atu
r
al
ev
o
l
u
tio
n
to
i
ter
ativ
ely
r
ef
in
e
c
o
n
tr
o
ller
p
ar
am
eter
s
,
r
esu
ltin
g
in
co
n
tr
o
lle
r
s
th
at
d
y
n
am
ically
ad
ap
t
to
ch
a
n
g
in
g
co
n
d
itio
n
s
.
Mo
r
eo
v
er
,
th
e
in
teg
r
atio
n
o
f
m
ac
h
in
e
lear
n
in
g
tech
n
i
q
u
es
e
n
ab
les
co
n
tr
o
ller
s
to
lear
n
f
r
o
m
d
ata,
e
n
h
an
cin
g
ad
ap
tab
ilit
y
to
co
m
p
lex
an
d
ev
o
l
v
in
g
s
y
s
tem
s
[
4
]
.
GAs
h
av
e
em
er
g
ed
as
p
r
o
m
in
en
t
o
p
tim
izatio
n
tech
n
iq
u
e
s
,
d
em
o
n
s
tr
atin
g
th
eir
ef
f
ec
ti
v
en
ess
in
im
p
r
o
v
in
g
th
e
ef
f
icien
c
y
o
f
f
u
zz
y
co
n
t
r
o
ller
s
[
5
]
.
B
y
m
im
ic
k
in
g
th
e
ev
o
lu
tio
n
ar
y
p
r
o
ce
s
s
,
GAs
g
en
er
ate
a
n
d
r
ef
in
e
p
o
te
n
tial
s
o
lu
tio
n
s
o
v
e
r
s
u
cc
ess
iv
e
iter
atio
n
s
.
I
n
th
e
co
n
tex
t
o
f
FLC
o
p
tim
izatio
n
,
GAs
ef
f
icien
tly
n
av
ig
ate
t
h
e
v
ast
p
ar
am
eter
s
p
ac
e,
y
ield
in
g
co
n
tr
o
ller
s
with
en
h
an
ce
d
p
e
r
f
o
r
m
an
ce
an
d
ad
ap
tab
ilit
y
.
T
h
eir
ab
ilit
y
to
h
an
d
le
m
u
lti
-
d
im
e
n
s
io
n
al,
n
o
n
-
lin
ea
r
o
p
tim
izatio
n
p
r
o
b
lem
s
h
as
estab
lis
h
ed
GAs
as
in
d
is
p
en
s
ab
le
to
o
ls
in
d
esig
n
in
g
r
o
b
u
s
t a
n
d
e
f
f
icien
t f
u
zz
y
co
n
tr
o
ller
s
[
6
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
7
,
No
.
2
,
Feb
r
u
a
r
y
20
2
5
:
78
1
-
7
8
8
782
I
n
th
is
ar
ticle,
we
p
r
esen
t
a
n
o
v
el
m
eth
o
d
f
o
r
d
esig
n
i
n
g
m
e
m
b
er
s
h
ip
f
u
n
ctio
n
s
u
s
in
g
a
GA
with
o
u
t
im
p
o
s
in
g
co
n
s
tr
ain
ts
.
T
h
e
p
a
p
er
is
s
tr
u
ctu
r
ed
in
to
s
ix
s
ec
tio
n
s
.
Sectio
n
1
o
u
tlin
es
t
h
e
p
r
o
b
lem
ad
d
r
ess
ed
in
th
is
s
tu
d
y
.
Sectio
n
2
p
r
o
v
id
es
a
r
ev
iew
o
f
r
ele
v
an
t
liter
atu
r
e
,
h
ig
h
lig
h
tin
g
m
ajo
r
c
o
n
tr
ib
u
tio
n
s
an
d
f
in
d
in
g
s
in
th
e
f
ield
.
Sectio
n
3
id
e
n
tifie
s
u
n
s
o
lv
ed
p
r
o
b
lem
s
an
d
a
r
ea
s
r
eq
u
ir
in
g
im
p
r
o
v
em
en
t,
p
ar
ticu
lar
ly
th
o
s
e
ad
d
r
ess
ed
in
o
u
r
m
a
n
u
s
cr
ip
t.
Sectio
n
4
d
escr
ib
es
o
u
r
n
e
w
co
n
tr
ib
u
tio
n
s
,
d
etailin
g
t
h
e
n
o
v
el
m
eth
o
d
f
o
r
d
esig
n
in
g
m
em
b
e
r
s
h
ip
f
u
n
ctio
n
s
u
s
in
g
GAs.
Sectio
n
5
o
u
tli
n
es
h
o
w
th
e
s
u
b
s
eq
u
en
t
s
ec
tio
n
s
d
em
o
n
s
tr
ate
o
u
r
ap
p
r
o
ac
h
an
d
its
r
elev
an
ce
,
wh
ile
s
ec
tio
n
6
co
n
cl
u
d
es
th
e
p
ap
er
b
y
d
is
cu
s
s
in
g
th
e
r
a
m
if
icatio
n
s
o
f
o
u
r
f
in
d
in
g
s
an
d
th
eir
im
p
licatio
n
s
f
o
r
f
u
tu
r
e
r
esear
ch
an
d
ap
p
lic
atio
n
s
.
2.
G
E
NE
T
I
C
A
L
G
O
RI
T
H
M
T
h
e
GA
is
a
p
o
wer
f
u
l
to
o
l
i
n
ar
tific
ial
in
tellig
en
ce
,
d
r
aw
in
g
in
s
p
ir
atio
n
f
r
o
m
C
h
ar
les
Dar
win
'
s
th
eo
r
y
o
f
n
atu
r
al
ev
o
lu
tio
n
[
7
]
.
I
t
o
p
er
ates
as
a
r
an
d
o
m
ized
s
ea
r
ch
tech
n
iq
u
e,
e
x
p
lo
r
in
g
v
ar
io
u
s
p
ar
ts
o
f
t
h
e
s
o
lu
tio
n
s
p
ac
e
with
d
if
f
e
r
en
t
s
o
lu
tio
n
ch
ar
ac
ter
is
tics
to
f
in
d
o
p
tim
al
an
s
wer
s
iter
ativ
ely
[
8
]
.
At
its
co
r
e
a
r
e
g
en
etic
o
p
er
ato
r
s
lik
e
cr
o
s
s
o
v
er
an
d
m
u
tatio
n
,
m
ir
r
o
r
in
g
th
e
b
io
lo
g
ical
p
r
o
ce
s
s
es
o
f
r
ep
r
o
d
u
ctio
n
an
d
g
en
etic
v
ar
iatio
n
.
T
h
r
o
u
g
h
s
elec
tio
n
,
f
it
in
d
i
v
id
u
als
ar
e
ch
o
s
en
t
o
p
r
o
d
u
ce
o
f
f
s
p
r
in
g
,
in
h
e
r
itin
g
th
eir
tr
aits
an
d
p
o
ten
tially
im
p
r
o
v
in
g
u
p
o
n
th
em
[
9
]
.
T
h
is
iter
ativ
e
p
r
o
ce
s
s
m
im
ics
n
atu
r
al
s
elec
tio
n
,
g
r
a
d
u
ally
r
ef
i
n
in
g
th
e
p
o
p
u
latio
n
to
war
d
s
o
p
tim
al
s
o
lu
tio
n
s
[
1
0
]
.
GA'
s
ab
ilit
y
to
in
h
er
it
in
f
o
r
m
atio
n
ac
r
o
s
s
iter
atio
n
s
s
ets
it
ap
ar
t,
en
ab
lin
g
s
y
n
c
h
r
o
n
o
u
s
o
p
e
r
atio
n
o
n
m
u
ltip
le
tar
g
ets
an
d
f
a
cilitatin
g
g
lo
b
al
o
p
tim
izatio
n
[
1
1
]
.
B
y
lev
er
a
g
in
g
th
ese
ev
o
lu
tio
n
a
r
y
p
r
in
cip
les,
GA
p
r
o
v
es
to
b
e
a
v
er
s
atile
a
n
d
ef
f
e
ctiv
e
h
e
u
r
is
tic
o
p
tim
iz
atio
n
tech
n
iq
u
e
f
o
r
s
o
lv
in
g
co
m
p
lex
p
r
o
b
lem
s
[
1
2
]
,
[
1
3
]
.
T
h
e
o
p
tim
izatio
n
p
r
o
c
ess
o
u
tlin
ed
co
n
s
is
ts
o
f
s
ev
er
al
k
ey
s
tep
s
:
−
I
n
itializatio
n
:
an
in
itial
p
o
p
u
latio
n
o
f
in
d
iv
id
u
als,
r
ep
r
esen
tin
g
ac
tiv
e
p
o
wer
g
en
er
ated
b
y
u
n
its
an
d
FC
s
y
s
tem
lev
els,
is
r
an
d
o
m
ly
s
elec
ted
with
in
d
ef
in
e
d
lim
its
.
−
E
v
alu
atio
n
:
ea
ch
in
d
iv
id
u
al'
s
f
itn
ess
is
ass
e
s
s
ed
u
s
in
g
an
o
b
jectiv
e
f
u
n
ctio
n
,
wh
ic
h
in
th
is
s
tu
d
y
m
ea
s
u
r
es
t
h
e
to
tal
d
aily
o
p
er
atin
g
c
o
s
t d
er
iv
ed
f
r
o
m
o
p
tim
al
p
o
wer
f
lo
w
ca
lcu
latio
n
s
.
−
T
er
m
in
atio
n
:
th
e
p
r
o
ce
s
s
ch
ec
k
s
f
o
r
p
r
ed
ef
in
e
d
s
to
p
p
in
g
co
n
d
itio
n
s
;
if
m
et,
th
e
iter
atio
n
s
to
p
s
;
o
th
er
wis
e,
it p
r
o
ce
ed
s
.
−
Selectio
n
:
in
d
iv
id
u
als
ar
e
r
an
k
ed
b
ased
o
n
f
itn
ess
,
an
d
th
o
s
e
with
h
ig
h
s
co
r
es
p
r
o
g
r
ess
to
th
e
n
ex
t
s
tep
f
o
r
cr
o
s
s
o
v
er
.
−
C
r
o
s
s
o
v
er
:
a
n
ew
p
o
p
u
latio
n
o
f
o
f
f
s
p
r
i
n
g
is
g
en
er
ate
d
b
y
co
m
b
in
in
g
c
h
ar
ac
ter
is
tics
o
f
s
elec
ted
in
d
iv
id
u
als
u
s
in
g
g
en
etic
p
r
in
cip
les an
d
s
p
ec
if
ied
cr
o
s
s
o
v
er
s
tr
ateg
ies.
−
Mu
tatio
n
:
n
ew
o
f
f
s
p
r
in
g
u
n
d
er
g
o
m
u
tatio
n
to
i
n
tr
o
d
u
ce
d
iv
er
s
ity
,
e
x
p
lo
r
in
g
n
ew
s
e
ar
ch
ar
ea
s
a
n
d
p
r
ev
en
tin
g
co
n
v
er
g
e
n
ce
to
lo
c
al
m
in
im
a.
−
B
est
s
o
lu
tio
n
:
th
e
f
ittes
t
in
d
iv
id
u
als
ar
e
s
tatis
tically
s
elec
te
d
to
r
ep
lace
th
e
cu
r
r
en
t
p
o
p
u
latio
n
,
with
th
e
b
est
s
o
lu
tio
n
s
to
r
ed
.
I
ter
atio
n
co
n
ti
n
u
es
u
n
til
th
e
m
ax
im
u
m
n
u
m
b
er
o
f
iter
atio
n
s
is
r
ea
ch
ed
o
r
s
to
p
p
in
g
cr
iter
ia
ar
e
m
et
[
1
4
]
,
[
1
5
]
.
3.
F
UZ
Z
Y
CO
N
T
RO
L
L
E
R
Dr
.
L
.
A.
Z
ad
e
h
,
a
Pro
f
ess
o
r
at
th
e
Un
iv
er
s
ity
o
f
C
alif
o
r
n
ia
at
B
er
k
eley
,
in
tr
o
d
u
ce
d
t
h
e
co
n
ce
p
t
o
f
f
u
zz
y
s
et
th
eo
r
y
an
d
f
u
zz
y
co
n
tr
o
l
d
u
r
in
g
th
e
1
9
6
0
s
[
1
6
]
.
His
in
n
o
v
ativ
e
ap
p
r
o
ac
h
in
v
o
lv
ed
e
x
ten
d
in
g
B
o
o
lean
lo
g
ic
to
a
co
n
tin
u
o
u
s
f
o
r
m
,
allo
win
g
f
o
r
a
s
m
o
o
t
h
tr
an
s
itio
n
b
etwe
en
0
an
d
1
,
th
u
s
e
n
ab
lin
g
th
e
d
ef
in
itio
n
o
f
s
o
f
t
b
o
u
n
d
ar
ies.
T
h
ese
s
o
f
t
b
o
u
n
d
a
r
ies
p
av
ed
th
e
way
f
o
r
th
e
cr
ea
tio
n
o
f
FLCs
,
wh
ich
h
av
e
f
o
u
n
d
in
cr
ea
s
in
g
ap
p
licatio
n
s
in
s
y
s
tem
s
with
n
o
n
lin
ea
r
ity
an
d
u
n
ce
r
tain
ty
.
FLCs
ar
e
d
es
i
g
n
ed
b
ased
o
n
th
e
h
u
m
an
o
p
er
ato
r
'
s
f
am
iliar
ity
an
d
u
n
d
er
s
tan
d
in
g
[
1
7
]
.
Fu
zz
y
co
n
tr
o
l
u
tili
ze
s
f
u
n
d
a
m
en
tal
p
r
in
cip
les
o
f
f
u
zz
y
lo
g
ic
to
g
o
v
e
r
n
a
p
lan
t
o
r
s
y
s
tem
u
s
in
g
lin
g
u
is
tic
v
ar
iab
les
in
s
tead
o
f
s
tr
ict
m
ath
em
atica
l
eq
u
atio
n
s
[
1
8
]
.
T
h
is
ap
p
r
o
ac
h
i
n
v
o
lv
es
r
ep
r
esen
tin
g
,
m
an
ip
u
latin
g
,
an
d
ap
p
ly
i
n
g
t
h
e
ex
p
e
r
ien
tial
k
n
o
wled
g
e
o
f
h
u
m
an
s
t
o
ac
h
iev
e
ef
f
ec
tiv
e
co
n
tr
o
l
o
v
er
th
e
s
y
s
tem
.
FLCs
ar
e
co
m
p
r
is
ed
o
f
f
o
u
r
p
r
im
a
r
y
co
m
p
o
n
en
ts
:
−
Fu
zz
if
ier
:
th
e
f
u
zz
if
ier
tak
es
r
ea
l
-
v
alu
ed
in
p
u
t
d
ata
an
d
tr
an
s
f
o
r
m
s
it
in
to
f
u
zz
y
s
ets.
T
h
is
m
ath
em
atica
l
p
r
o
ce
s
s
,
k
n
o
wn
as
f
u
zz
if
icatio
n
,
co
n
v
e
r
ts
an
elem
en
t
in
th
e
u
n
iv
er
s
e
o
f
d
is
co
u
r
s
e
in
to
th
e
co
r
r
esp
o
n
d
in
g
m
em
b
er
s
h
ip
v
alu
e
o
f
th
e
f
u
zz
y
s
et
[
1
9
]
.
−
Kn
o
wled
g
e
b
ase:
f
u
zz
y
lo
g
ic
u
tili
ze
s
a
s
tr
aig
h
tf
o
r
war
d
r
u
le
f
o
r
m
at,
s
u
ch
as
"
I
F
x
is
a
AN
D
y
is
b
T
HE
N
z
is
c,
"
to
ad
d
r
ess
co
n
tr
o
l
p
r
o
b
lem
s
with
o
u
t
th
e
n
ee
d
f
o
r
co
m
p
lex
m
ath
em
atica
l
m
o
d
ellin
g
.
T
h
e
Kn
o
wled
g
eb
ase
co
m
p
r
is
es
tw
o
ess
en
tial
elem
en
ts
:
a
d
atab
a
s
e
d
ef
in
in
g
th
e
m
em
b
er
s
h
ip
f
u
n
ctio
n
s
,
an
d
a
r
u
le
b
ase
co
n
tain
i
n
g
f
u
zz
y
r
u
l
es c
o
n
s
tr
u
cted
to
g
u
id
e
th
e
c
o
n
tr
o
l a
ctio
n
s
[
2
0
]
.
−
I
n
f
er
en
ce
e
n
g
in
e:
th
e
in
f
e
r
en
c
e
en
g
in
e
estab
lis
h
es
a
co
n
n
ec
ti
o
n
b
etwe
en
th
e
p
r
em
is
es
o
f
f
u
zz
y
s
ets
an
d
th
eir
co
r
r
esp
o
n
d
in
g
c
o
n
s
eq
u
e
n
ce
s
.
I
t
d
eter
m
in
es
wh
ich
co
n
tr
o
l
r
u
le
to
ac
tiv
ate
an
d
h
o
w
th
e
lo
g
ic
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
Desig
n
in
g
fu
z
z
y
mem
b
ers
h
ip
fu
n
ctio
n
s
u
s
in
g
g
en
etic
a
lg
o
r
ith
m
…
(
A
li Ha
med
)
783
co
n
n
ec
tio
n
s
h
o
u
ld
b
e
estab
lis
h
ed
b
etwe
en
th
e
p
r
em
is
e
co
m
p
o
n
en
ts
.
Dep
en
d
in
g
o
n
th
e
m
eth
o
d
u
s
ed
to
d
e
t
e
r
m
i
n
e
t
h
e
o
u
t
p
u
t
,
t
h
e
i
n
f
e
r
en
c
e
s
y
s
t
e
m
c
a
n
b
e
c
l
ass
i
f
i
e
d
as
e
i
t
h
e
r
M
a
m
d
a
n
i
-
t
y
p
e
o
r
S
u
g
e
n
o
-
t
y
p
e
[
2
1
]
.
−
Def
u
zz
if
ier
:
th
e
d
e
f
u
zz
if
ier
co
n
v
er
ts
th
e
o
u
tp
u
t
f
u
zz
y
s
ets
in
to
a
p
r
ec
is
e,
cr
is
p
v
alu
e.
T
h
is
tr
an
s
f
o
r
m
atio
n
p
r
o
ce
s
s
,
k
n
o
wn
as
d
ef
u
zz
if
icatio
n
,
in
v
o
lv
es
co
n
v
er
tin
g
f
u
zz
y
s
ets
in
to
cr
is
p
s
ets.
Sev
er
al
d
ef
u
zz
if
icatio
n
m
eth
o
d
s
ex
is
t,
in
clu
d
in
g
ce
n
tr
o
i
d
,
b
is
ec
to
r
,
m
ea
n
o
f
m
ax
im
a,
s
m
allest
o
f
m
ax
im
a,
an
d
lar
g
est o
f
m
ax
im
a
[
2
2
]
.
Fig
u
r
e
1
illu
s
tr
ates
th
e
s
tr
u
ctu
r
e
o
f
a
f
u
zz
y
co
n
tr
o
ller
,
wh
ic
h
in
v
o
lv
es
d
ef
i
n
in
g
two
in
p
u
t
v
ar
iab
les:
th
e
er
r
o
r
d
e
n
o
ted
as
'
e'
an
d
th
e
ch
an
g
e
o
f
e
r
r
o
r
r
ep
r
esen
ted
as
'
d
e.
'
T
h
e
o
u
tp
u
t
v
ar
iab
le
is
lab
eled
'
u
.
'
On
th
e
o
th
er
h
an
d
,
Fig
u
r
e
2
d
is
p
lay
s
th
e
in
p
u
t
an
d
o
u
tp
u
t
v
a
r
iab
l
es
o
f
a
Fu
zz
y
s
y
s
tem
[
2
3
]
,
[
2
4
]
.
T
h
e
e
r
r
o
r
a
n
d
ch
an
g
e
o
f
er
r
o
r
in
p
u
ts
ar
e
ca
teg
o
r
ized
i
n
to
s
ev
e
n
m
em
b
e
r
s
h
ip
f
u
n
ctio
n
s
ea
ch
,
n
a
m
ely
:
b
ig
n
e
g
ativ
e
(
B
N)
,
m
ed
iu
m
n
e
g
ativ
e
(
MN
)
,
s
m
all
n
eg
ativ
e
(
SN)
,
ze
r
o
(Z
E
)
,
s
m
all
p
o
s
itiv
e
(
SP
)
,
m
ed
iu
m
p
o
s
itiv
e
(
MP)
an
d
b
ig
p
o
s
itiv
e
(
B
P).
Similar
ly
,
th
e
o
u
tp
u
t v
ar
ia
b
le
'
u
'
is
al
s
o
d
iv
id
ed
in
to
th
e
s
am
e
s
ev
e
n
m
em
b
e
r
s
h
ip
f
u
n
cti
o
n
s
.
Fig
u
r
e
1
.
I
n
p
u
t a
n
d
o
u
tp
u
t
v
ar
i
ab
les
4.
E
NCO
DI
NG
O
F
M
E
M
B
E
R
SH
I
P
F
UNCT
I
O
NS
4.1.
C
onven
t
i
ona
l
m
et
ho
d
E
n
co
d
in
g
in
g
e
n
etic
r
ep
r
esen
t
atio
n
in
v
o
lv
es
s
tr
u
ctu
r
in
g
p
ar
am
eter
s
in
to
ch
r
o
m
o
s
o
m
es
o
r
s
tr
in
g
s
to
r
ep
r
esen
t
s
o
lu
tio
n
s
.
T
h
ese
ch
r
o
m
o
s
o
m
es
ty
p
ically
co
n
tain
o
r
g
an
ized
s
eq
u
e
n
ce
s
o
f
p
ar
a
m
eter
s
.
I
n
a
s
p
ec
if
ic
s
y
s
tem
u
tili
zin
g
tr
ian
g
le
-
ty
p
e
m
em
b
er
s
h
ip
f
u
n
ctio
n
s
,
t
h
e
p
a
r
am
eter
s
d
eter
m
in
in
g
th
e
s
h
a
p
e
o
f
ea
c
h
tr
ian
g
le
ar
e
cr
u
cial,
in
clu
d
in
g
th
e
h
o
r
i
zo
n
tal
co
o
r
d
i
n
ate
o
f
th
e
v
er
te
x
(
x
2
)
,
lef
t
b
ase
p
o
i
n
t
(
x
1
)
,
a
n
d
r
ig
h
t
b
ase
p
o
in
t
(x
3
)
,
as
d
ep
icted
in
Fig
u
r
e
2
.
Fig
u
r
e
2
.
R
ep
r
esen
tatio
n
o
f
tr
i
an
g
u
lar
m
e
m
b
er
s
h
ip
f
u
n
ctio
n
As
d
ep
icted
in
Fig
u
r
e
2
,
th
e
m
em
b
er
s
h
ip
f
u
n
ctio
n
is
d
ef
in
ed
u
s
in
g
th
e
t
h
r
ee
v
a
r
iab
les,
x
1
,
x
2
,
an
d
x
3
,
with
in
th
e
in
ter
v
al
[
a
,
b
]
,
a
n
d
it c
an
b
e
d
ef
in
e
d
as f
o
llo
ws
(
1
)
[
2
5
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
7
,
No
.
2
,
Feb
r
u
a
r
y
20
2
5
:
78
1
-
7
8
8
784
{
−
1
2
−
1
1
≤
<
2
3
−
3
−
2
2
≤
≤
3
0
(
1
)
F
u
r
t
h
e
r
m
o
r
e
,
e
n
s
u
r
i
n
g
t
h
e
t
r
ia
n
g
u
l
a
r
s
h
a
p
e'
s
i
n
t
e
g
r
i
t
y
,
m
em
b
e
r
s
h
i
p
f
u
n
c
t
i
o
n
s
m
u
s
t
a
d
h
e
r
e
t
o
c
o
n
s
t
r
a
i
n
t
s
s
u
c
h
a
s
(
2
)
.
{
1
<
2
2
<
3
1
>
1
3
<
2
(
2
)
Ho
wev
er
,
g
e
n
etic
o
p
er
ato
r
s
m
ay
o
cc
asio
n
ally
p
r
o
d
u
ce
ch
r
o
m
o
s
o
m
es
th
at
v
io
late
th
ese
co
n
s
tr
ain
ts
,
lead
in
g
to
th
e
r
ejec
tio
n
o
f
g
e
n
es
ass
o
ciate
d
with
illeg
al
ch
r
o
m
o
s
o
m
es.
Fo
r
a
s
y
s
tem
wi
th
2
1
m
em
b
e
r
s
h
ip
f
u
n
ctio
n
s
,
84
co
n
s
tr
ain
ts
a
r
e
n
ee
d
ed
.
I
f
an
y
o
f
th
ese
co
n
s
t
r
ain
ts
ar
e
n
o
t
s
atis
f
ied
,
th
e
c
h
r
o
m
o
s
o
m
e
will
b
e
r
ejec
ted
,
ev
en
if
it
c
o
n
tain
s
o
p
tim
al
v
alu
es
f
o
r
o
th
er
f
u
n
cti
o
n
s
.
T
h
is
ca
n
p
r
o
lo
n
g
o
p
tim
iz
atio
n
tim
e
an
d
y
ield
u
n
ce
r
tain
r
esu
lts
.
E
ac
h
f
u
n
ctio
n
is
d
eter
m
in
e
d
b
y
3
p
ar
am
eter
s
:
x
1
,
x
2
,
an
d
x
3
.
T
h
er
ef
o
r
e,
p
r
ev
io
u
s
r
e
s
ea
r
ch
h
as
s
o
u
g
h
t
to
o
p
tim
ize
th
ese
p
ar
a
m
eter
s
to
en
h
an
ce
t
h
e
m
em
b
e
r
s
h
ip
f
u
n
ctio
n
s
.
Ho
wev
e
r
,
i
n
o
u
r
s
o
lu
tio
n
,
we
d
id
n
o
t a
ttem
p
t to
o
p
tim
ize
th
ese
p
ar
am
eter
s
.
4.2.
Propo
se
d
m
et
ho
d
I
n
o
u
r
s
o
lu
tio
n
,
we
av
o
i
d
ed
r
e
ly
in
g
o
n
th
e
p
o
in
ts
x
1
,
x
2
,
x
3
,
u
n
lik
e
p
r
ev
io
u
s
m
eth
o
d
th
at
u
tili
ze
th
e
co
n
s
tr
ain
ts
.
I
n
d
e
ed
,
t
h
e
u
s
e
o
f
co
n
s
tr
ain
ts
ca
n
d
im
in
is
h
th
e
p
er
f
o
r
m
an
ce
o
f
th
e
GA
.
T
o
m
itig
ate
th
e
im
p
ac
t
o
f
co
n
s
tr
ain
ts
o
n
th
e
GA
,
it h
as b
ee
n
s
u
g
g
ested
n
o
t to
em
p
lo
y
t
h
em
.
T
h
is
allo
ws f
o
r
g
r
ea
ter
f
lex
ib
ilit
y
.
As s
h
o
wn
in
Fig
u
r
e
3
w
e
ass
i
g
n
ed
ea
ch
x
p
o
in
t a
r
an
g
e
o
f
l
en
g
th
'
m
'
Fo
r
ex
am
p
le
,
f
o
r
t
h
e
p
o
in
t x
1
,
it
v
ar
ies
b
etwe
en
p
1
an
d
p
1
+m
;
f
o
r
x
2
,
th
e
r
an
g
e
is
[
p
2
,
p
2
+m
]
,
an
d
f
o
r
x
3
,
t
h
e
r
an
g
e
is
[
p
3
,
p
3
+m
]
.
Fro
m
th
e
F
ig
u
r
e
3
,
it c
an
b
e
o
b
s
er
v
ed
t
h
at
:
{
1
=
1
+
1
2
=
2
+
2
3
=
3
+
3
(
3
)
wh
er
e
p
o
i
n
ts
P1
,
P2
,
an
d
P3
ar
e
co
n
s
tan
ts
.
T
h
e
d
ec
is
io
n
t
o
ch
o
o
s
e
0
.
3
1
f
o
r
th
e
r
a
n
g
e
'
M'
d
ep
en
d
s
o
n
th
e
n
u
m
b
er
o
f
b
its
in
b
in
a
r
y
e
n
co
d
in
g
.
W
ith
5
b
its
,
we
ca
n
r
ep
r
esen
t
3
2
v
alu
es
f
r
o
m
0
t
o
3
1
,
an
d
b
y
d
iv
id
in
g
th
e
v
alu
e
b
y
1
0
0
.
T
h
e
p
a
r
am
eter
s
r
1
,
r
2
,
a
n
d
r
3
v
ar
y
b
etwe
en
0
an
d
0
.
3
1
.
I
n
t
h
is
ca
s
e,
th
e
r
ep
r
esen
tatio
n
o
f
a
m
em
b
er
s
h
ip
f
u
n
ctio
n
with
in
a
ch
r
o
m
o
s
o
m
e
is
ac
h
iev
ed
u
s
in
g
th
e
p
ar
am
eter
s
R
1
,
R
2
,
a
n
d
R
3
,
r
ath
er
th
an
x
1
,
x
2
,
an
d
x
3
,
as illu
s
tr
ated
in
Fig
u
r
e
4
.
Fig
u
r
e
3
.
Pro
p
o
s
ed
p
r
esen
tatio
n
f
o
r
m
em
b
er
s
h
i
p
f
u
n
ctio
n
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
Desig
n
in
g
fu
z
z
y
mem
b
ers
h
ip
fu
n
ctio
n
s
u
s
in
g
g
en
etic
a
lg
o
r
ith
m
…
(
A
li Ha
med
)
785
Fig
u
r
e
4
.
C
h
r
o
m
o
s
o
m
e
s
tr
u
ctu
r
e
Fro
m
Fig
u
r
e
5
,
it
is
ev
id
en
t
th
at
th
e
u
tili
ze
d
m
em
b
er
s
h
ip
f
u
n
ctio
n
s
ar
e
s
y
m
m
etr
ic.
E
ac
h
m
em
b
er
s
h
ip
f
u
n
ctio
n
h
as
its
o
wn
p
ar
a
m
et
er
s
,
s
u
ch
as
r
1
,
r
2
,
an
d
r
3
,
w
h
ich
d
ef
i
n
e
its
s
h
ap
e
an
d
p
o
s
itio
n
r
elativ
e
to
th
e
ax
is
.
T
h
e
v
alu
es
o
f
th
ese
p
ar
am
eter
s
ca
n
v
ar
y
f
r
o
m
o
n
e
m
em
b
er
s
h
ip
f
u
n
ctio
n
to
an
o
th
e
r
.
Fo
r
ex
am
p
le,
th
e
s
ec
o
n
d
p
o
i
n
t
X
S
p2
o
f
th
e
'
s
m
all
p
o
s
itiv
e'
m
em
b
er
s
h
ip
f
u
n
ctio
n
is
p
r
esen
ted
as f
o
llo
ws:
2
=
2
+
2
(
4
)
d
u
e
to
th
e
s
y
m
m
etr
y
o
f
th
e
f
u
n
ctio
n
s
,
it c
an
also
b
e
s
tated
th
at:
2
=
2
(
5
)
Fig
u
r
e
5
.
R
ep
r
esen
tatio
n
o
f
s
y
m
m
etr
ic
m
em
b
er
s
h
ip
f
u
n
ctio
n
s
Neg
ativ
e
m
em
b
er
s
h
i
p
f
u
n
ctio
n
s
ar
e
n
o
t
co
n
s
id
er
e
d
in
th
e
o
p
tim
izatio
n
p
r
o
ce
s
s
,
an
d
th
eir
p
ar
am
eter
s
ar
e
n
o
t
in
clu
d
e
d
in
th
e
ch
r
o
m
o
s
o
m
e.
Af
ter
o
p
tim
izatio
n
,
th
ese
f
u
n
ctio
n
s
a
r
e
r
ec
o
n
s
tr
u
cte
d
f
r
o
m
th
e
p
o
s
itiv
e
f
u
n
ctio
n
s
.
T
a
b
le
1
co
n
tain
s
th
e
g
en
etic
p
ar
am
eter
s
'
v
alu
es u
tili
ze
d
in
th
is
s
tu
d
y
.
T
ab
le
1
.
Par
am
eter
s
o
f
GA
P
a
r
a
me
t
e
r
V
a
l
u
e
P
o
p
u
l
a
t
i
o
n
s
i
z
e
20
N
u
mb
e
r
o
f
g
e
n
e
r
a
t
i
o
n
s
4
0
0
M
u
t
a
t
i
o
n
0
.
1
C
r
o
ss
o
v
e
r
0
.
8
T
h
e
f
itn
ess
f
u
n
ctio
n
em
p
lo
y
e
d
in
th
is
co
n
tex
t
is
th
e
in
teg
r
al
s
q
u
ar
e
o
f
t
h
e
er
r
o
r
,
as
d
e
f
in
ed
b
y
th
e
f
o
llo
win
g
:
=
∫
2
(
6
)
th
is
f
itn
ess
f
u
n
ctio
n
m
ea
s
u
r
es
th
e
cu
m
u
lativ
e
s
q
u
a
r
ed
d
is
cr
ep
an
cy
b
etwe
en
th
e
ac
tu
al
a
n
d
ex
p
ec
te
d
o
u
tp
u
ts
,
p
r
o
v
id
i
n
g
a
co
m
p
r
eh
en
s
iv
e
e
v
alu
a
tio
n
o
f
th
e
s
y
s
tem
'
s
p
er
f
o
r
m
an
ce
ac
r
o
s
s
v
ar
io
u
s
co
n
d
itio
n
s
o
r
d
atasets
.
Fig
u
r
e
6
p
r
o
v
id
es
a
v
is
u
al
r
e
p
r
esen
tatio
n
o
f
t
h
e
f
u
zz
y
m
e
m
b
er
s
h
ip
,
wh
ich
h
as
b
ee
n
r
e
f
in
ed
an
d
o
p
tim
ized
th
r
o
u
g
h
th
e
iter
ativ
e
p
r
o
ce
s
s
o
f
a
GA
o
v
er
a
s
p
an
o
f
2
0
0
g
e
n
er
atio
n
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
7
,
No
.
2
,
Feb
r
u
a
r
y
20
2
5
:
78
1
-
7
8
8
786
Fig
u
r
e
6
.
Op
tim
ized
m
em
b
e
r
s
h
ip
f
u
n
ctio
n
s
: e
r
r
o
r
m
em
b
e
r
s
h
ip
,
d
er
iv
ativ
e
o
f
er
r
o
r
m
em
b
er
s
h
ip
an
d
o
u
tp
u
t
m
e
m
b
er
s
h
ip
5.
RE
SU
L
T
AND
DI
SCU
SS
I
O
N
Af
ter
em
p
lo
y
in
g
a
GA
to
r
ef
in
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9.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
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4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
7
,
No
.
2
,
Feb
r
u
a
r
y
20
2
5
:
78
1
-
7
8
8
788
B
I
O
G
RAP
H
I
E
S O
F
AUTH
O
RS
Ali
H
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r
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g
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c
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c
a
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c
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tac
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a
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in
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s
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.
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tell
ig
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c
a
n
b
e
c
o
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tac
ted
a
t
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m
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:
c
h
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riet.
a
h
m
e
d
@u
n
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v
-
b
e
c
h
a
r.
d
z
.
Evaluation Warning : The document was created with Spire.PDF for Python.