Information Technology Reference
In-Depth Information
d
i
positions occupied by a
x
d
i
is represented by
d
i
followed by (
d
i
−
1) zeros
(for example,
x
3
= [300]), and (b) (1 +
x
) is represented by -1. The
pseudo-
chromosome format
of the
MACA
is illustrated in
Fig. 4
.
3.3
Crossover Algorithm
The crossover algorithm implemented is similar in nature to the conventional
one normally used for
GA
framework with minor modifications as illustrated in
Fig.5
. The algorithm takes two
MACA
from the present population (
PP
) and
forms the resultant
MACA
. The
pseudo-chromosome format
has
x
d
i
represented
by
d
i
followed by (
d
i
−
1) zeros. But in the case of
Fig 5c
, we have 3 followed by
a single zero. This is a violation since the property of
MACA
is not maintained.
So we take out those two symbols and form a
CA
of elementary divisor
x
2
and
adjust it. The resultant
MACA
after adjustment is shown in
Fig 5d.
(a)
2
0
-1
3
0
0
-1
1
-1
1
0
MACA1
(b)
2
0
-1
2
0
-1
1
-1
3
0
0
MACA2
(c)
2
0
-1
3
0
-1
1
-1
3
0
0
(d)
2
0
-1
2
0
-1
1
-1
3
0
0
1
2
3
4
5
6
7
8
9
10
11
Fig. 5.
An Example of Cross-over Technique
3.4
Mutation Algorithm
The mutation algorithm emulates the normal mutation scheme (
Fig.6
. It makes
some minimal change in the existing
MACA
of
PP
(Present Population) to a
new
MACA
for
NP
(Next Population). Similar to the single point mutation
scheme, the
MACA
is mutated at a single point.
In mutation algorithm, an (
x
+ 1)'s position is altered. Some anomaly crops
up due to its alteration. The anomaly is resolved to ensure that after mutation
the new
CA
is also an
MACA
. The inconsistent format, as shown in the
Fig 6b
is the mutated version of
Fig 6a
. The inconsistency of the
pseudo-chromosome
format
of
Fig 6b
can be resolved to generate the format of
Fig 6c
.
