  Genetic algorithms are considered as an original way to solve problems,
probably because of their generality and of their "blind" nature. But GAs are
also unusual since the features of many implementations (among all that could
be thought of) are principally led by the biological metaphor, while efficiency
measurements intervene only afterwards. We propose here to examine the
relevance of these biomimetic aspects, by pointing out some fundamental
similarities and divergences between GAs and the genome of living beings shaped
by natural selection. One of the main differences comes from the fact that GAs
rely principally on the so-called implicit parallelism, while giving to the
mutation/selection mechanism the second role. Such differences could suggest
new ways of employing GAs on complex problems, using complex codings and
starting from nearly homogeneous populations.
