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consists obviously of the independent variables plus the ephemeral random
constant “?”, thus giving T = {a, b, c, d, e, f, g, h, i, j, ?}. The set of random
numerical constants R = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, and “?” ranged over the
rational interval [-1, 1]. The performance and the parameters used in the
acellular experiments are shown in Table 7.6 and in the multicellular sys-
tems in Table 7.7.
And as you can see, all the four systems perform quite well at this difficult
task and considerably better than all the STROGANOFF systems studied in
Table 7.6
General settings for the sunspots prediction task using the basic gene expres-
sion algorithm (
GEA-B
) and the GEP-RNC algorithm (
GEP-RNC
).
GEA-B
GEP-RNC
Number of runs
100
100
Number of generations
5,000
5,000
Population size
100
100
Number of fitness cases
90 (Table 7.3)
90 (Table 7.3)
Function set
4(+ - * /)
4(+ - * /)
Terminal set
a-j
a-j ?
Random constants array length
--
10
Random constants type
--
Rational
Random constants range
--
[-1, 1]
Head length
7
7
Gene length
15
23
Number of genes
3
3
Linking function
+
+
Chromosome length
45
69
Mutation rate
0.044
0.044
Inversion rate
0.1
0.1
IS transposition rate
0.1
0.1
RIS transposition rate
0.1
0.1
One-point recombination rate
0.3
0.3
Two-point recombination rate
0.3
0.3
Gene recombination rate
0.3
0.3
Gene transposition rate
0.1
0.1
Dc-specific mutation rate
--
0.044
Dc-specific inversion rate
--
0.1
Dc-specific transposition rate
--
0.1
Random constants mutation rate
--
0.01
Fitness function
Eq. (3.5)
Eq. (3.5)
Average best-of-run fitness
5.64991
6.70823
Average best-of-run R-square
0.8798383868
0.8803029253
