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giving F = {U, D, T}. The terminal set consisted of T = {a, b, c, d, e, f},
representing, respectively, {a
0
, a
1
, d
0
, d
1
, d
2
, d
3
}. Furthermore, a set of 10
random weights, drawn from the interval [-2, 2] and represented as usual by
the numerals 0-9 were used, giving W = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}.
For this study, we are going to use both a unigenic and a multigenic sys-
tem in order to show the performance and evolvability of multigenic neural
network systems on this difficult task. Both the performance and parameters
used per run in both experiments are shown in Table 10.3.
Table 10.3
Parameters for the 6-multiplexer using a unigenic and a multigenic system.
Unigenic
Multigenic
Number of runs
100
100
Number of generations
2000
2000
Population size
50
50
Number of fitness cases
64 (Table 10.2)
64 (Table 10.2)
Function set
3(U D T)
3(U D T)
Terminal set
a b c d e f
a b c d e f
Linking function
--
Or
Weights array length
10
10
Weights range
[-2, 2]
[-2, 2]
Head length
17
5
Number of genes
1
4
Chromosome length
103
124
Mutation rate
0.044
0.044
Inversion rate
0.1
0.1
One-point recombination rate
0.3
--
Intragenic two-point recombination rate
0.3
0.6
Gene recombination rate
--
0.1
Gene transposition rate
--
0.1
IS transposition rate
0.1
0.1
RIS transposition rate
0.1
0.1
Dw mutation rate
0.044
0.044
Dw-specific inversion rate
0.1
0.1
Dw-specific transposition rate
0.1
0.1
Weights mutation rate
0.002
0.002
Fitness function
Eq. (3.10)
Eq. (3.10)
Success rate
4%
6%
