Information Technology Reference
In-Depth Information
individual's progeny that might exhibit new traits and thus be better adapted
to the natural environment. And the better adapted an organism becomes, the
higher the probability of being selected and leaving more offspring. The
variation or genetic diversity we find in nature among organisms is, in fact,
the raw material for selection as any organism in the struggle for existence
exploits any advantage it may have upon others to guarantee its survival. The
more successful individuals leave more progeny and these better adapted
organisms (and the criterion for better adapted is that they survived) may
increase in frequency in the population, altering its character with time. But,
in nature, the process of adaptation never comes to a rest due to the fact that
organisms not only change the same environment in which selection occurs
but also because more individuals are produced than can survive. Even in
stable ecosystems evolution is under way.
In evolutionary computation the term “fitness” is widely used but its mean-
ing differs from the current meaning in evolution theory today. In evolution-
ary computation the term fitness has the meaning it had in Darwin's day: a
quality of organisms likely to be favored by selection. In fact, in all artificial
genetic algorithms individuals are selected according to this fitness. In evo-
lution theory, though, fitness is a measure that incorporates both survival and
reproductive success.
This shows a very important difference between adaptive computer sys-
tems and natural systems. In nature, organisms are selected against a mul-
titude of factors and why or how a new trait is selected is not always clear.
Therefore the fitness of an individual can only be measured by the progeny
it leaves. But in computer systems the fitness of an individual in a certain
environment is easily evaluated, and this measure can be rigorously used to
determine selection. However, some scientists like to introduce a random
factor in selection to mimic natural selection, and a simple way of imple-
menting this kind of selection is by roulette-wheel sampling (see, e.g.,
Goldberg 1989). Each individual receives a slice of a circular roulette-
wheel proportional to its fitness. The roulette is spun, and the bigger the
slice the higher the probability of being selected. And, as it happens with
all non-deterministic phenomena, sometimes the improbable happens
whereas the highly probable does not happen. Nevertheless, I prefer this
kind of selection because it mimics nature more faithfully and works very
well in all populations (different selection schemes will be discussed in
chapter 12). Indeed, this kind of selection, together with the cloning of the
