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I
S
S
N:
2
5
0
2
-
4
7
52
HE
C
:
a
hy
br
id
e
v
olut
ionar
y
-
C
E
L
F
algor
it
hm
f
or
e
nhanc
e
d
inf
luenc
e
max
imiz
ati
on
in
…
(
J
y
oti
Sunil
M
or
e
)
809
I
n
t
h
i
s
pa
pe
r
,
we
pr
op
o
s
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o
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hy
b
r
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d
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l
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a
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y
-
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L
F
(
H
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)
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l
go
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i
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t
h
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.
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h
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f
o
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y
s
t
r
a
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.
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vi
d
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g
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us
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od
f
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r
i
nf
l
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n
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e
m
a
xi
mi
z
a
t
i
o
n
.
Ou
r
c
o
n
tr
i
b
ut
i
o
n
s
i
nc
l
ude
:
i
)
t
h
e
de
v
e
l
o
pm
e
n
t
o
f
t
h
e
H
E
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a
l
gor
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,
wh
i
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s
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t
h
C
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to
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nf
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c
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m
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t
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pr
o
b
l
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m
m
o
r
e
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f
f
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c
t
i
v
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l
y
;
i
i
)
a
n
e
m
p
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t
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de
m
o
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s
t
r
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t
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t
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a
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t
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e
HE
C
a
l
gor
i
t
hm
o
u
tper
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or
m
s
tr
a
di
t
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o
n
a
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m
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t
h
o
ds
,
i
n
c
l
ud
in
g
gr
e
e
dy
a
l
go
r
i
t
hm
s
,
C
E
L
F
,
a
n
d
c
uc
k
oo
s
e
a
r
c
h
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i
n
t
e
r
m
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o
f
i
nf
l
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pr
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d
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o
m
put
a
t
i
o
n
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l
e
f
f
i
c
i
e
n
c
y
;
i
ii
)
a
c
o
m
pr
e
h
e
n
s
i
ve
a
n
a
l
y
s
i
s
o
f
t
h
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pe
r
f
or
m
a
n
c
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i
m
pr
o
v
e
m
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n
t
s
p
r
o
vi
de
d
by
t
h
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HE
C
a
l
go
r
i
t
h
m
a
c
r
os
s
v
a
r
i
o
us
n
e
t
w
or
k
s
tr
uc
tu
r
e
s
.
2.
L
I
T
E
RA
T
UR
E
RE
VI
E
W
T
h
e
pr
o
bl
e
m
o
f
i
nf
l
ue
n
c
e
m
a
xim
i
z
a
t
i
o
n
h
a
s
b
e
e
n
e
x
t
e
ns
i
ve
ly
r
e
s
e
a
r
c
h
e
d
i
n
s
o
c
i
a
l
n
e
t
wo
r
k
a
n
a
l
y
s
i
s
.
He
r
e
t
h
e
go
a
l
i
s
to
i
de
n
t
i
f
y
a
s
m
a
l
l
s
u
bs
e
t
o
f
n
o
de
s
(
s
e
e
d
n
o
de
s
)
t
h
a
t
c
a
n
m
a
xim
i
z
e
t
h
e
s
pr
e
a
d
o
f
i
nf
l
ue
n
c
e
t
h
r
o
ugh
o
u
t
t
h
e
n
e
t
wo
r
k
.
K
e
m
p
e
e
t
al.
[
1]
f
o
r
m
a
li
z
e
d
t
hi
s
pr
o
bl
e
m
a
n
d
i
n
t
r
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t
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gr
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lgo
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whi
c
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pr
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vi
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ppr
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n
b
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put
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t
i
o
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a
ll
y
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pe
ns
i
ve
.
M
e
t
a
h
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lgo
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t
hm
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h
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s
ge
ne
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c
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l
go
r
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t
hm
s
(
GA
)
,
pa
r
t
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c
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wa
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m
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p
t
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mi
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a
t
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o
n
(
P
S
O)
,
a
n
d
a
n
t
c
o
l
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ny
o
pt
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mi
z
a
t
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o
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(
AC
O)
h
a
v
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x
p
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r
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d
f
o
r
i
nf
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n
c
e
m
a
xim
i
z
a
t
i
o
n
i
n
s
o
c
i
a
l
n
e
t
wo
r
ks
[
1]
.
T
h
e
s
e
a
ppr
o
a
c
h
e
s
b
a
l
a
nc
e
b
e
t
we
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n
s
o
l
ut
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o
n
qua
li
t
y
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n
d
c
o
m
put
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t
i
o
n
a
l
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f
f
i
c
i
e
n
c
y
,
of
f
e
r
i
ng
a
l
t
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na
t
i
ve
s
t
o
t
r
a
di
t
i
o
na
l
gr
e
e
d
y
a
l
go
r
i
t
hm
s
[
2]
.
He
ur
i
s
t
i
c
m
e
t
h
o
ds
t
h
a
t
l
e
v
e
r
a
ge
ne
t
wor
k
c
h
a
r
a
c
t
e
r
i
s
t
i
c
s
s
uc
h
a
s
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o
de
c
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t
r
a
l
i
t
y
(
e
.
g.
,
de
gr
e
e
c
e
n
t
r
a
l
i
t
y
,
b
e
t
we
e
nn
e
s
s
c
e
n
t
r
a
l
i
t
y
)
o
r
l
o
c
a
l
s
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r
c
h
t
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v
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f
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t
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i
nf
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ue
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t
i
a
l
n
o
de
s
i
n
s
o
c
i
a
l
n
e
t
wo
r
ks
[
3
]
-
[
6
]
.
A
ddr
e
s
s
i
n
g
t
h
e
c
h
a
ll
e
n
g
e
s
po
s
e
d
by
bi
g
da
t
a
e
nvi
r
o
nm
e
n
t
s
,
i
n
c
l
ud
i
ng
l
a
r
ge
-
s
c
a
l
e
s
o
c
i
a
l
n
e
t
wor
ks
,
m
e
t
a
he
ur
i
s
t
i
c
o
p
t
i
mi
z
a
t
i
o
n
t
e
c
hni
que
s
a
r
e
e
x
p
l
o
r
e
d
f
o
r
e
f
f
i
c
i
e
n
t
a
n
d
s
c
a
l
a
bl
e
s
o
l
ut
i
o
n
s
[
7]
,
[
8]
.
R
e
c
e
n
t
s
t
udi
e
s
h
a
v
e
f
o
c
us
e
d
o
n
e
nh
a
n
c
in
g
m
e
t
a
h
e
ur
i
s
t
i
c
a
l
go
r
i
t
hm
s
s
pe
c
if
i
c
a
ll
y
f
o
r
i
nf
l
ue
n
c
e
m
a
xim
i
z
a
t
i
o
n
.
T
h
e
s
e
e
nh
a
n
c
e
m
e
n
t
s
t
y
p
i
c
a
ll
y
i
nvo
l
v
e
i
n
t
e
gr
a
t
i
n
g
do
m
a
i
n
-
s
pe
c
if
i
c
k
n
o
w
l
e
dge
o
r
opt
i
mi
z
i
ng
a
l
go
r
i
t
hm
pa
r
a
m
e
t
e
r
s
to
b
e
tt
e
r
s
ui
t
t
h
e
c
h
a
r
a
c
t
e
r
i
s
t
i
c
s
o
f
s
o
c
i
a
l
n
e
t
wo
r
ks
[
9]
-
[
12]
.
A
ddr
e
s
s
i
n
g
m
u
l
t
i
p
l
e
o
bj
e
c
t
i
v
e
s
,
s
uc
h
a
s
m
a
xi
mi
z
i
ng
i
nf
l
ue
nc
e
s
pr
e
a
d
whil
e
m
i
n
im
i
z
i
ng
c
o
m
put
a
t
i
o
n
a
l
r
e
s
o
ur
c
e
s
or
c
o
n
s
i
d
e
r
i
n
g
d
i
f
f
e
r
e
n
t
pr
o
pa
ga
t
i
o
n
m
o
de
l
s
,
i
s
c
r
uc
i
a
l
i
n
pr
a
c
t
i
c
a
l
a
pp
li
c
a
t
i
o
ns
o
f
i
nf
l
ue
n
c
e
m
a
xim
i
z
a
t
i
o
n
[
13]
-
[
16
]
.
P
a
r
a
l
l
e
l
a
n
d
d
i
s
t
r
i
b
ut
e
d
c
o
m
put
i
n
g
f
r
a
m
e
wo
r
ks
f
o
r
m
e
t
a
h
e
ur
i
s
t
i
c
a
l
go
r
i
t
hm
s
c
a
n
s
i
g
nif
i
c
a
n
t
l
y
e
nh
a
n
c
e
s
c
a
l
a
bil
i
t
y
a
n
d
e
f
f
i
c
i
e
nc
y
i
n
i
n
f
l
u
e
n
c
e
m
a
xim
i
z
a
t
i
o
n
t
a
s
ks
o
n
l
a
r
ge
n
e
t
wo
r
ks
[
17]
-
[
19]
.
R
o
b
us
t
o
p
t
i
mi
z
a
t
i
o
n
t
e
c
hni
que
s
w
i
t
hi
n
m
e
t
a
he
ur
i
s
t
i
c
f
r
a
m
e
wo
r
ks
e
n
s
ur
e
t
h
e
r
e
l
iabili
t
y
a
n
d
s
t
a
bi
li
t
y
o
f
i
nf
l
ue
nc
e
m
a
xim
i
z
a
t
i
o
n
s
o
l
ut
i
o
ns
a
ga
i
ns
t
un
c
e
r
t
a
i
n
t
i
e
s
a
n
d
v
a
r
i
a
t
i
o
n
s
i
n
n
e
t
wo
r
k
c
o
n
d
i
t
i
o
n
s
[
20]
-
[
22]
.
I
n
t
e
gr
a
t
i
n
g
m
e
t
a
h
e
ur
i
s
t
i
c
a
l
go
r
i
t
hm
s
w
i
t
h
m
a
c
hi
ne
l
e
a
r
ni
ng
t
e
c
hni
que
s
,
s
uc
h
a
s
r
e
i
nf
o
r
c
e
m
e
n
t
l
e
a
r
ni
ng
o
r
de
e
p
l
e
a
r
ni
ng,
e
x
p
l
o
r
e
s
n
o
v
e
l
a
ppr
o
a
c
h
e
s
to
i
nf
l
u
e
n
c
e
m
a
xi
mi
z
a
t
i
o
n
i
n
s
o
c
i
a
l
n
e
t
wo
r
ks
[
23]
.
A
ddr
e
s
s
i
ng
pr
a
c
t
i
c
a
l
c
o
n
s
t
r
a
i
n
t
s
s
uc
h
a
s
b
udge
t
li
mi
t
a
t
i
o
ns
o
r
r
e
s
o
ur
c
e
c
o
n
s
t
r
a
i
n
t
s
r
e
qu
i
r
e
s
m
e
t
a
h
e
ur
i
s
t
i
c
a
l
go
r
i
t
hm
s
c
a
pa
bl
e
o
f
o
pt
i
m
i
z
i
ng
i
nf
l
ue
n
c
e
s
pr
e
a
d
whil
e
a
d
h
e
r
i
ng
t
o
s
pe
c
i
f
i
e
d
c
o
ns
t
r
a
i
n
t
s
[
24]
.
Dr
a
w
i
n
g
i
ns
p
i
r
a
t
i
o
n
f
r
o
m
bi
o
l
o
g
i
c
a
l
s
y
s
t
e
m
s
,
m
e
t
a
h
e
ur
i
s
t
i
c
a
l
go
r
i
t
hm
s
s
uc
h
a
s
a
r
t
i
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n
do
m
s
e
l
e
c
t
i
o
ns
f
r
o
m
t
h
e
gr
a
ph
)
.
P
o
pul
a
t
i
o
n
S
i
z
e
:
l
e
t
t
h
e
i
ni
t
i
a
l
po
pu
l
a
t
i
o
n
b
e
a
s
e
t
P
=
{S
1
,
S
2
,
…,
S
n
}
wh
e
r
e
e
a
c
h
S
i
i
s
a
c
a
n
d
i
d
a
t
e
s
e
e
d
s
e
t
o
f
s
i
z
e
k.
c)
F
i
t
n
e
s
s
e
v
a
l
ua
t
i
o
n
:
i
nf
l
ue
n
c
e
s
p
r
e
a
d
s
i
m
u
l
a
t
i
o
n
:
f
or
e
a
c
h
c
a
n
d
i
da
te
s
e
e
d
s
e
t
S
i
∈
P
,
s
i
m
u
l
a
t
e
i
nf
l
ue
n
c
e
s
pr
e
a
d
(
us
i
n
g
t
h
e
p
r
opa
ga
t
i
o
n
m
o
d
e
l
)
a
n
d
c
o
m
p
u
te
i
t
s
f
i
t
n
e
s
s
f
(
S
i
)
,
wh
i
c
h
i
s
t
h
e
tot
a
l
n
um
b
e
r
o
f
i
nf
l
ue
n
c
e
d
n
o
d
e
s
i
n
t
h
e
n
e
tw
or
k
.
F
or
b
e
s
t
f
i
t
n
e
s
s
,
tr
a
c
k
t
h
e
s
e
e
d
s
e
t
w
i
t
h
th
e
hi
gh
e
s
t
i
nf
l
ue
n
c
e
s
p
r
e
a
d
S
b
e
s
t
.
d)
E
v
o
l
u
t
i
o
n
a
r
y
op
t
i
m
i
z
a
t
i
o
n
:
p
e
r
f
o
r
m
tou
r
n
a
m
e
n
t
s
e
l
e
c
t
i
o
n
to
c
h
oos
e
pa
r
e
n
t
s
e
e
d
s
e
t
s
b
a
s
e
d
on
t
h
e
i
r
f
i
t
n
e
s
s
s
c
or
e
s
.
C
r
os
s
ov
e
r
:
c
om
b
i
n
e
tw
o
p
a
r
e
n
t
s
e
e
d
s
e
ts
b
y
ta
k
i
n
g
th
e
u
n
i
o
n
of
b
oth
,
th
e
n
r
a
n
d
o
m
l
y
s
e
l
e
c
t
k
n
o
d
e
s
f
r
o
m
th
e
c
om
b
i
n
e
d
s
e
t.
M
u
ta
ti
on
:
wi
th
a
s
m
a
l
l
p
r
ob
a
b
i
l
i
t
y
,
r
a
n
d
o
m
l
y
s
wa
p
on
e
or
tw
o
n
od
e
s
i
n
th
e
s
e
e
d
s
e
t
wi
th
o
th
e
r
n
od
e
s
i
n
th
e
n
e
tw
or
k
.
N
e
w
P
op
u
l
a
t
i
on
:
t
h
e
n
e
w
p
op
u
l
a
t
i
on
P
′
i
s
f
or
m
e
d
b
y
r
e
tai
n
i
n
g
th
e
f
i
tt
e
s
t
i
n
di
vi
d
u
a
l
s
f
r
o
m
th
e
c
u
r
r
e
n
t
p
op
u
l
a
t
i
on
a
n
d
a
d
di
n
g
of
f
s
p
r
i
n
g
g
e
n
e
r
a
te
d
th
r
ou
g
h
c
r
os
s
ov
e
r
a
n
d
m
u
ta
ti
on
.
e)
F
i
t
n
e
s
s
-
ba
s
e
d
pr
uni
ng
:
f
o
r
e
a
c
h
s
e
e
d
s
e
t
i
n
t
h
e
n
e
w
po
pul
a
t
i
o
n
,
r
e
-
e
v
a
l
ua
t
e
i
t
s
f
i
t
n
e
s
s
.
P
r
un
e
l
ow
-
pe
r
f
o
r
m
i
ng
c
a
n
d
i
da
t
e
s
a
n
d
r
e
t
a
i
n
t
h
e
to
p
n
s
e
t
s
.
D
y
n
a
mi
c
po
pu
l
a
t
i
o
n
s
i
z
e
:
o
p
t
i
o
n
a
ll
y
,
t
h
e
po
pul
a
t
i
o
n
s
i
z
e
c
a
n
b
e
d
y
na
mi
c
a
ll
y
r
e
duc
e
d
a
f
t
e
r
e
a
c
h
i
t
e
r
a
t
i
o
n
to
f
o
c
us
o
n
hi
g
h
e
r
-
qua
li
t
y
s
e
e
d
s
e
t
s
.
f)
T
e
r
m
i
na
t
i
o
n
:
t
h
e
a
l
go
r
i
t
hm
t
e
r
m
i
na
t
e
s
whe
n
t
h
e
r
e
i
s
l
i
t
t
l
e
t
o
n
o
i
m
pr
o
v
e
m
e
n
t
i
n
i
nf
l
ue
nc
e
s
pr
e
a
d
o
v
e
r
m
u
l
t
i
p
l
e
ge
ne
r
a
t
i
o
ns
o
r
wh
e
n
a
s
e
t
n
u
m
b
e
r
o
f
i
t
e
r
a
ti
o
n
s
i
s
r
e
a
c
h
e
d.
4.
M
AT
HE
M
AT
I
CA
L
M
ODE
L
F
OR
T
HE
HE
C
AL
GO
RI
T
HM
4.
1.
I
n
f
l
u
e
n
c
e
s
p
r
e
ad
c
a
l
c
u
l
at
ion
L
e
t
G=
(
V,
E
)
b
e
t
h
e
gr
a
ph
wh
e
r
e
V
i
s
t
h
e
s
e
t
o
f
n
o
de
s
a
n
d
E
i
s
t
h
e
s
e
t
o
f
e
dge
s
.
De
f
i
ne
t
h
e
i
nf
l
ue
n
c
e
s
pr
e
a
d
f
u
nc
t
i
o
n
a
s
σ(
S
)
,
wh
e
r
e
S
⊆
V
i
s
a
s
e
t
o
f
s
e
e
d
n
o
de
s
.
T
h
e
i
nf
l
ue
nc
e
s
pr
e
a
d
σ(
S
)
r
e
pr
e
s
e
n
t
s
t
h
e
e
x
pe
c
t
e
d
n
u
m
be
r
o
f
n
o
de
s
i
nf
l
ue
n
c
e
d
by
t
h
e
s
e
e
d
s
e
t
S
un
de
r
a
di
f
f
u
s
i
o
n
m
o
de
l
,
s
uc
h
a
s
t
h
e
I
C
M
.
A
c
c
o
r
di
n
g
to
t
h
e
I
C
M
,
f
o
r
e
a
c
h
n
o
de
u
∈
V,
l
e
t
N(
u)
de
n
ot
e
t
h
e
s
e
t
o
f
n
e
i
g
hb
o
r
s
o
f
u.
L
e
t
p
uv
b
e
t
h
e
pr
o
b
a
bil
i
t
y
o
f
i
nf
l
ue
n
c
e
pr
o
pa
ga
t
i
o
n
f
r
o
m
n
o
de
u
to
n
o
de
v
.
T
h
e
s
pr
e
a
d
σ(
S
)
i
s
c
o
m
put
e
d
a
s
:
σ(
S
)
=
E
[
Num
b
e
r
of
n
o
de
s
i
nf
l
u
e
n
c
e
d
by
S
a
f
t
e
r
di
f
f
us
i
o
n
]
.
4.
2.
CE
L
F
in
it
ial
iz
at
ion
C
E
L
F
i
s
us
e
d
to
ge
n
e
r
a
t
e
hi
g
h
-
qua
l
i
t
y
i
n
i
t
i
a
l
s
e
e
d
s
e
t
s
e
f
f
i
c
i
e
n
t
l
y
.
L
e
t
C
E
L
F
(
G,
k
,
p)
b
e
t
h
e
C
E
L
F
a
l
go
r
i
t
hm
t
h
a
t
r
e
t
u
r
n
s
a
s
e
e
d
s
e
t
S
CE
L
F
o
f
s
i
z
e
k,
wh
e
r
e
p
i
s
t
h
e
i
nf
l
ue
nc
e
pr
o
pa
ga
t
i
o
n
pr
o
b
a
bil
i
t
y
.
C
E
L
F
pe
r
f
o
r
m
s
a
l
a
z
y
gr
e
e
d
y
a
ppr
o
a
c
h
t
o
a
ppr
o
xi
m
a
t
e
t
h
e
o
p
t
i
m
a
l
s
e
e
d
s
e
t
:
S
CE
L
F
=
C
E
L
F
(
G,
k,
p)
.
4.
3.
E
vol
u
t
ion
ar
y
op
t
im
iz
at
io
n
T
h
e
e
v
o
l
ut
i
o
n
a
r
y
c
o
m
po
n
e
n
t
r
e
f
i
ne
s
t
h
e
i
ni
t
i
a
l
s
e
e
d
s
e
t
S
CE
L
F
us
i
n
g
c
r
o
s
s
o
v
e
r
a
n
d
m
ut
a
t
i
o
n
o
pe
r
a
to
r
s
.
C
r
o
s
s
o
v
e
r
:
f
or
t
wo
pa
r
e
n
t
s
e
e
d
s
e
t
s
S
1
a
n
d
S
2
,
ge
n
e
r
a
t
e
a
c
hi
l
d
s
e
e
d
s
e
t
S
c
h
i
l
d
us
i
ng
a
c
r
o
s
s
o
v
e
r
po
i
n
t
c
:
S
c
h
i
l
d
=
S1[
0:
c
]
∪
S2
[
c
:
]
wh
e
r
e
S
1[
0:
c
]
de
n
ot
e
s
t
h
e
f
i
r
s
t
c
n
o
de
s
o
f
S
1
a
n
d
S
2[
c
:]
de
n
ot
e
s
t
h
e
r
e
m
a
i
n
i
ng
n
o
de
s
f
r
o
m
S
2.
E
n
s
u
r
e
∣
S
c
h
i
l
d
∣
=
k
by
r
e
m
o
vi
ng
dup
li
c
a
t
e
s
a
n
d/o
r
a
ddi
n
g
n
e
w
n
o
de
s
.
M
ut
a
t
i
o
n
:
f
o
r
a
s
e
e
d
s
e
t
S
,
pe
r
f
o
r
m
m
ut
a
t
i
o
n
w
i
t
h
pr
o
b
a
bil
i
t
y
m
.
R
a
n
do
m
ly
r
e
p
l
a
c
e
a
n
o
de
u
∈
S
wi
t
h
a
n
e
w
n
o
de
v
∈
V
∖
S
:
S
m
u
t
a
t
e
d
=
S
wi
t
h
n
o
de
u
r
e
pl
a
c
e
d
by
v
(
i
f
m
ut
a
t
i
o
n
)
F
i
t
n
e
s
s
f
u
n
c
t
i
o
n
:
e
va
l
u
a
t
e
t
h
e
f
i
t
n
e
s
s
o
f
s
e
e
d
s
e
t
s
S
us
i
n
g
t
h
e
i
nf
l
u
e
n
c
e
s
pr
e
a
d
σ(
S
)
:
F
it
ne
s
s
(
S)
=
σ
(
S)
P
a
r
a
m
e
t
e
r
t
uni
n
g
t
o
m
a
k
e
t
h
e
a
l
go
r
i
t
hm
pe
r
f
o
r
m
we
l
l
i
n
d
if
f
e
r
e
n
t
n
e
t
wor
k
e
n
vi
r
o
nm
e
n
t
s
,
c
e
r
t
a
i
n
pa
r
a
m
e
t
e
r
s
n
e
e
d
t
o
b
e
t
un
e
d
c
a
r
e
f
u
ll
y
.
He
r
e
’
s
a
b
r
e
a
kdo
wn
o
f
t
h
e
ke
y
pa
r
a
m
e
t
e
r
s
:
P
o
pul
a
t
i
o
n
s
i
z
e
(
n
)
:
i
t
r
e
p
r
e
s
e
n
t
s
t
h
e
n
u
m
be
r
o
f
s
e
e
d
s
e
t
s
m
a
i
n
t
a
i
ne
d
i
n
t
h
e
e
v
o
l
ut
i
o
n
a
r
y
po
pu
l
a
t
i
o
n
a
t
a
ny
po
i
n
t
.
L
a
r
ge
r
p
o
pul
a
t
i
o
n
s
i
m
pr
o
v
e
d
i
v
e
r
s
i
t
y
b
ut
i
n
c
r
e
a
s
e
c
o
m
put
a
t
i
o
n
a
l
c
o
s
t
.
S
m
a
ll
e
r
po
pul
a
t
i
o
n
s
r
e
duc
e
di
v
e
r
s
i
t
y
b
ut
r
un
f
a
s
t
e
r
.
He
n
c
e
i
t
i
s
n
e
c
e
s
s
a
r
y
to
m
a
i
n
t
a
i
n
a
t
r
a
d
e
o
f
f
.
S
t
a
r
t
wi
t
h
a
p
o
pul
a
t
i
o
n
s
i
z
e
o
f
n
=
10
×
k
(
wh
e
r
e
k
i
s
t
h
e
s
i
z
e
o
f
t
h
e
s
e
e
d
s
e
t
)
a
n
d
a
d
j
u
s
t
b
a
s
e
d
o
n
a
v
a
il
a
bl
e
c
o
m
put
a
t
i
o
n
a
l
r
e
s
o
ur
c
e
s
.
C
r
o
s
s
o
v
e
r
r
a
t
e
:
i
t
de
n
o
t
e
s
t
h
e
pr
o
p
o
r
t
i
o
n
o
f
t
h
e
p
o
pul
a
t
i
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HEC_Algorithm(G, k, prob, pop_size, gen_size, mutation_rate
):
1. Initialize population using CELF:
-
population = []
-
For i in range(pop_size):
seed_set = CELF_Algorithm(G, k, prob)
population.append(seed_set)
2. For generation in range(gen_size):
a. Evaluate fitness of each seed set in popu
lation:
-
fitness_scores = []
-
For each seed_set in population:
score = Fitness_Function(G, seed_set, prob)
fitness_scores.append(score)
b. Selection:
-
Sort population based on fitness_scores
-
Select top 50% of seed sets as parents
c. Crossover:
-
new_population = parents.copy()
-
While len(new_population) < pop_size:
parent1, parent2 = Randomly_Select_Parents(parents)
crossover_point = Random_Index(1
, k
-
1)
child = parent1[0:crossover_point] + parent2[crossover_point:]
Ensure_No_Duplicates(child)
new_population.append(child)
d. Mutation:
-
For each seed_set in new_population:
If Random() < mutation_rate
:
Mutate_A_Random_Node(seed_set)
e. Replace population with new_population
3. Ret
urn
the
seed
set
wit
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i:
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.
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r
in
g
, vo
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. 10, pp. 1852
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K
D
E
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.
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6]
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12
676, 2023, do
i:
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[
7]
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. 367
–
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614, 2016, do
i:
10.1016/j
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in
s
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[
8]
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.
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i:
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[
9]
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. 105554,
2
019, do
i:
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[
10]
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s
, v
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56, pp. 27
–
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586,
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.
126480,
20
22,
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i:
10.
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.
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.
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.
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.
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nt
e
ll
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nt
Sy
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te
m
s
and T
e
c
h
nol
ogy
, vo
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J
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L
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T
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C
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K
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ll
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nf
or
m
at
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n Sy
s
te
m
s
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t.
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E
.
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.
D
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om
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nal
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nt
e
ll
ig
e
nc
e
,
v
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J
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X
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Y
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L
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nd
Y
.
J
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G
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ll
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nt
Sy
s
te
m
s
,
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pp.
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P
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tt
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.
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or
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s
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l.
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o
. 1, Ar
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[
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.
3, A
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no
. 43, 2021, d
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i
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[
18]
W
.
M
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s
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.
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.
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.
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, 2022, do
i
:
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10489
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02203
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[
19]
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