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Ta b l e 3 . 9 . Bounded solutions
Solution
1
2
3
4
5
Index
1
1
3
3
5
4
2
5
4
3
2
2
3
3
4
1
1
3
4
3
1
5
3
5
5
1
5
5
2
1
6
2
1
1
4
2
7
3
4
5
5
2
8
2
5
3
5
5
9
3
3
3
3
2
10
1
5
1
1
3
Table 3.10. Replucated values
Solution
1
2
3
4
5
Index
1
1
3
3
5
4
2
5
4
3
2
2
3
3
4
1
1
3
4
3
1
5
3
5
5
1
5
5
2
1
6
2
1
1
4
2
7
3
4
5
5
2
8
2
5
3
5
5
9
3
3
3
3
2
10
1
5
1
1
3
Recursive mutation is applied in Step (5). For this illustration, the random mutation
schema is used as this was the most potent and also the most complicated.
The first routine is to drag all bound offending values to the offending boundary. The
boundary constraints are given as x ( lo ) = 1and x ( hi ) = 5 which is lower and upper bound
of the problem. Table 3.9 gives the bounded solution.
In random mutation , initially all the duplicated values are isolated as given in
Tab le 3 . 1 0.
The next step is to randomly set default values for each replication. For example,
in Solution 1, the value 3 is replicated in 2 indexes; 2 and 3. So a random number is
generated to select the default value of 3. Let us assume that index 3 is generated. In
this respect, only value 3 indexed by 2 is labelled as replicated. This routine is applied
to the entire population, solution piece wise in order to set the default values.
A possible representation can be given as in Table 3.11.
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