Information Technology Reference
In-Depth Information
Table 3.3
Parameters for the simple symbolic regression problem.
Number of generations
50
Population size
20
Number of fitness cases
10 (Table 3.2)
Function set
+ - * /
Terminal set
a
Head length
7
Number of genes
3
Linking function
+
Chromosome length
45
Mutation rate
0.044
Inversion rate
0.1
IS transposition rate
0.1
RIS transposition rate
0.1
Gene transposition rate
0.1
One-point recombination rate
0.4
Two-point recombination rate
0.2
Gene recombination rate
0.1
Fitness function
Eq. (3.3a)
Selection range
100
Precision
0.01
However, as we will see, one of the advantages of gene expression program-
ming is that it is capable of solving relatively complex problems using small
population sizes and, thanks to the compact Karva notation, it is easy to
analyze every single individual from a run.
Figure 3.30 shows the progression of average fitness and the fitness of the
best individual of the successful run we are going to analyze in this section.
As you can see in Figure 3.30, in this run, a perfect solution was found in
generation 10.
The initial population of this run and the fitness of each individual in the
particular environment of Table 3.2, is shown below (the best of generation
is shown in bold):
Genera
t
ion N: 0
012345678901234012345678901234012345678901234
-/a+*+*aaaaaaaa*++*/a+aaaaaaaa+++a*--aaaaaaaa-[ 0] = 285.2363
+a-*/+aaaaaaaaa+/*+/+*aaaaaaaa+/++++/aaaaaaaa-[ 1] = 324.4358
