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equal to 0.01. Thus, for the 10 fitness cases used in this problem (again the
training samples shown in Table 3.2),
f
max
= 1000. The complete list of the
parameters used per run is shown in Table 5.4.
The evolutionary dynamics of the successful run we are going to analyze
is shown in Figure 5.8. And as you can see, in this run, a perfect solution was
found in generation 7.
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):
Generation N: 0
01234567890123456789012
//*++++aa??a???82565470-{7,9,3,8,7,8,0,2,7,5}-[ 0] = 820.4595
*//?a++?a?a???a28979219-{8,7,5,9,3,8,5,7,0,2}-[ 1] = 824.7692
Table 5.4
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 ?
Random constants array length
10
Random constants range
Integer interval [0, 9]
Head length
7
Gene length
23
Number of genes
1
Chromosome length
23
Mutation rate
0.044
Inversion rate
0.1
IS transposition rate
0.1
RIS transposition rate
0.1
One-point recombination rate
0.3
Two-point recombination rate
0.3
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
Equation (3.3a)
Selection range
100
Precision
0.01
