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Note that not only the total length of the noncoding regions increases from
the most compact to the less compact but also increases the number of neu-
tral motifs. Specifically, the length of the noncoding region in the unigenic
solution is only six and no neutral motifs are present; for the three-genic
solution, the total length of the noncoding regions is 16 and three neutral
motifs involving a total of 12 nodes can be counted; for the five-genic solu-
tion, the noncoding regions encompass already 66 elements and there are six
neutral motifs involving a total of 31 nodes.
Because of their clarity, the results presented in this section are extremely
useful for understanding the role of genetic neutrality both in artificial and
natural evolution. As shown here, there are two different kinds of neutral
regions in GEP: the neutral motifs within the ORFs and the noncoding re-
gions at the end of the ORFs. In simple replicator systems such as GP or
GAs, only the former exists whereas in genotype/phenotype systems such as
the DNA/protein system or GEP, both kinds exist. And the presence of
noncoding regions in genotype/phenotype systems is certainly entangled with
the higher efficiency observed in these systems. For instance, introns in DNA
are believed to be excellent targets for crossover, allowing the recombina-
tion of different building blocks without their disruption (e.g., Maynard Smith
and Szathmáry 1995). The noncoding regions of GEP genes can also be used
for this purpose and, indeed, whenever the crossover points are chosen within
these regions, entire ORFs are exchanged. Furthermore, the noncoding re-
gions of GEP genes are ideal places for the accumulation of neutral muta-
tions that can be later activated and integrated into coding regions. This is an
excellent source of genetic variation and certainly contributes to the increase
in performance observed in redundant systems.
But, at least in gene expression programming, the noncoding regions play
another, much more fundamental role: they allow the modification of the
genome by numerous high performing genetic operators. And here by “high
performing” I mean genetic operators that always produce valid structures.
This problem of valid structures applies only to artificial evolutionary sys-
tems for in nature there is no such thing as an invalid protein. How and why
the DNA/protein system got this way is not known, but certainly there were
