The General Philosophy of the Artificial Adaptive Systems
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 mean 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 microprocesses, simulating the original process functioning. Such models organise themselves in space and time and connect in a nonlinear 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 formalise 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 is 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 system models in natural computation. For this reason we present in this chapter an application of new artificial adaptive systems to a very hard and pragmatic topic: drug trafficking. That because we think that “real world” is often the best theory.
KeywordsGenetic Algorithm Genetic Programming Gradient Descent Candidate Solution Vector Quantisation
- Tóth, G. J. and Lõrincz, A. (1995). Genetic algorithm with migration on topology conserving maps. Neural Netw. World 2, 171–181.Google Scholar
- Eldredge, N. (1995). The great debate at the high table of evolutionary theory. New York: John Wiley & Sons.Google Scholar
- Reetz, B (1993). Greedy solutions to the travelling sales persons problem. Advanced Technology for Developers 2, 8–14.Google Scholar