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Table 7.4
Settings used in the GEP simulation of the original STROGANOFF (
GEP-OS
) and
the GEP simulation of the enhanced STROGANOFF using a unigenic system (
GEP-
ESU
) and a multigenic system (
GEP-ESM
).
GEP-OS
GEP-ESU
GEP-ESM
Number of runs
100
100
100
Number of generations
5000
5000
5000
Population size
100
100
100
Number of fitness cases
90 (Table 7.3)
90 (Table 7.3)
90 (Table 7.3)
Function set
16(F9)
F1 - F16
F1 - F16
Terminal set
a-j
a-j
a-j
Random constants array length
120
120
40
Random constants type
Rational
Rational
Rational
Random constants range
[-1,1]
[-1,1]
[-1,1]
Head length
21
21
7
Gene length
169
169
57
Number of genes
1
1
3
Linking function
--
--
+
Chromosome length
169
169
171
Mutation rate
0.044
0.044
0.044
Inversion rate
0.1
0.1
0.1
IS transposition rate
0.1
0.1
0.1
RIS transposition rate
0.1
0.1
0.1
One-point recombination rate
0.3
0.3
0.3
Two-point recombination rate
0.3
0.3
0.3
Gene recombination rate
--
--
0.3
Gene transposition rate
--
--
0.1
Dc-specific mutation rate
0.06
0.06
0.06
Dc-specific inversion rate
0.1
0.1
0.1
Dc-specific transposition rate
0.1
0.1
0.1
Random constants mutation rate
0.25
0.25
0.25
Fitness function
Eq. (3.5)
Eq. (3.5)
Eq. (3.5)
Average best-of-run fitness
0.95220
5.30666
5.81873
Average best-of-run R-square
0.2530739782
0.8472991731
0.8566823219
Indeed, continuing our discussion about the importance of random numeri-
cal constants in evolutionary modeling, there is a simple experiment we could
do. We could implement the bivariate polynomials with unit coefficients
presented in Table 7.2 and try to evolve complex polynomial solutions with
them (Table 7.5). And one thing that immediately strikes us is that both the
fitness and the R-square of every single best-of-run solution have exactly the
same value that was obtained for average best-of-run fitness and average
best-of-run R-square. This obviously means that the system is trapped in
