Information Technology Reference
In-Depth Information
Table 5.6
Performance and settings used in the sequence induction problem.
GEA-B
GEP-NC
GEP-RNC
Number of runs
100
100
100
Number of generations
100
100
100
Population size
30
30
30
Number of fitness cases
10 (Table 5.5)
10 (Table 5.5)
10 (Table 5.5)
Function set
+ - * /
+ - * /
+ - * /
Terminal set
n
n 0 1 2 3
n ?
Random constants array length
--
--
10
Random constants type
--
--
Integer
Random constants range
--
--
[0, 3]
Head length
6
6
6
Gene length
13
13
20
Number of genes
5
5
5
Linking function
+
+
+
Chromosome length
65
65
100
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
0.3
0.3
Gene transposition rate
0.1
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)
Eq. (3.5)
Average best-of-run fitness
979.472
376.751
766.561
Average best-of-run R-square
0.9999884864
0.999700006
0.999980023
Success rate
97%
35%
75%
with a precision of 0.01, for instance) this kind of problem and, therefore, the
performance of all the three approaches will be compared in terms of aver-
age best-of-run fitness and average best-of-run R-square. For this problem, a
set of 20 random fitness cases chosen from the interval [-1, 1] was used
(Table 5.7). Again, the mean squared error, evaluated by equation (3.4a), was
used as basis for the fitness function, with the fitness being evaluated by
equation (3.5) and, therefore,
f
max
= 1000. This experiment, with its three
different approaches, is summarized in Table 5.8.
