This chapter has the objective of describing the structure and placing in a taxonomy the Artificial
Adaptive Systems (AAS). These systems form part of the vast world of Artificial Intelligence (AI) nowadays
called more properly Artificial Sciences (AS). Artificial Sciences means those sciences for which an understanding
of natural and/or cultural processes is achieved by the recreation of those processes through automatic
models. In particular, Natural Computation tries to construct automatic models of complex processes,
using the local interaction of elementary micro-processes, simulating the original process functioning. Such
models organize themselves in space and time and connect in a non-linear way to the global process they are
part of, trying to reproduce the complexity through the dynamic creation of specific and independent local
rules that transform themselves in relation to the dynamics of the process. Natural Computation constitutes
the alternative to Classical Computation (CC). This one, in fact, has great difficulty in facing natural/cultural
processes, especially when it tries to impose external rules to understand and reproduce them, trying to formalize
these processes in an artificial model. In Natural Computation ambit, Artificial Adaptive Systems are
theories which generative algebras are able to create artificial models simulating natural phenomenon. The
learning and growing process of the models is isomorphic to the natural process evolution, that is, it’s itself
an artificial model comparable with the origin of the natural process. We are dealing with theories adopting
the “time of development” of the model as a formal model of “time of process” itself. Artificial Adaptive Systems
comprise Evolutive Systems and Learning Systems. Artificial Neural Networks are the more diffused
and best-known Learning Systems models in Natural Computation.
Keywords: Artificial Adaptive Systems, Atrificial Neural Networks, Genetic Algorithms, Evolutionary Algorithms,
Natural Computation.