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In the first successful run of this experiment, a solution with maximum
fitness was discovered in generation 16:
012345 012345
536241 EDCFBA (11.13)
which corresponds to the best assignment of 44 (see also Figure 11.7 above):
5
E
3
D
6
C
2
F
4
B
1
A
11.4 Evolutionary Dynamics of Simple GEP Systems
We concluded the last chapter by analyzing the evolutionary dynamics of one
of the most complex GEP systems - multigenic neural networks - and ob-
served that GEP-nets exhibit exactly the same kind of evolutionary dynamics
found in less complex genotype/phenotype systems.
As stated earlier in this chapter, the simple chromosomal organization
used for combinatorial optimization is very similar to the canonical GA.
Thus, it would be interesting to see if the evolutionary dynamics of these
simpler GEP systems are also of the kind observed in GA's populations.
Let's first analyze the simplest GEP system in which only one multigene
family is used per chromosome. Its evolutionary dynamics is shown in Fig-
ure 11.8. And curiously enough, the evolutionary dynamics for this simple
system is similar to the dynamics characteristic of GA's populations. As you
can see, for this kind of dynamics the plot for average fitness closely accom-
panies the plot for best fitness and the oscillatory pattern on average fitness
is less pronounced.
And finally, let's analyze a slightly more complex system where chromo-
somes composed of two multigene families are used. The evolutionary dy-
namics shown in Figure 11.9 was obtained for the first successful run of the
experiment summarized in Table 11.2. It is worth noticing that this
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