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Table 12.5
Comparing the performance of unigenic and multigenic systems on the se-
quence induction problem.
1 Gene
3 Genes
5 Genes
Number of runs
100
100
100
Number of generations
100
100
100
Population size
50
50
50
Number of fitness cases
10
10
10
Function set
+ - * /
+ - * /
+ - * /
Head length
37
12
7
Number of genes
1
3
5
Linking function
--
+
+
Chromosome length
75
75
75
Mutation rate
0.03
0.03
0.03
One-point recombination rate
0.3
0.3
0.3
Two-point recombination rate
0.3
0.3
0.3
Gene recombination rate
--
0.1
0.1
IS transposition rate
0.1
0.1
0.1
IS elements length
1,2,3
1,2,3
1,2,3
RIS transposition rate
0.1
0.1
0.1
RIS elements length
1,2,3
1,2,3
1,2,3
Gene transposition rate
--
0.1
0.1
Selection range
25%
25%
25%
Precision
0%
0%
0%
Success rate
41%
79%
96%
12.6 The Open-ended Evolution of GEP Populations
We have already seen that the populations of gene expression programming
can be made to evolve efficiently because the genetic operators allow the
permanent introduction of new material in the genetic pool. Here we are
going to explore this question further, analyzing the variation of success rate
with evolutionary time.
In this section, again was used the sequence induction problem of section
5.6.1 as it is a considerably difficult problem with an exact solution.
In the first analysis, the dependence of success rate on evolutionary time
was analyzed for healthy and strong populations of 50 and 250 individuals
each (Figure 12.13). In this experiment, all the genetic operators were switched
on. The rates used are shown in the first column of Table 12.6.
