In nature, neural networks are neurons, some of which are connected to sensory organs, such as eyes, ears, noses, and so on. The pattern of neurons that are active and those that are not is the way the organism represents whatever it is experiencing. As a result of experience, neurons that are simultaneously active tend to grow connections to each other. Recently, computers have become fast enough to simulate the genetic process for very simple cases. Basically, the main features of a product are considered genes, which are combined in a chain to form a chromosome.
Different products are chromosomes with different genes in different places.
The resulting chromosome is then tested and, if it is better according to some criteria, it is assigned a high priority for mating with still other highly rated chromosomes. This process is repeated thousands or even millions of times-a computer simulation of the evolutionary process. A classic use of genetic algorithms is in the design of aircraft, since the components of aircraft can be clearly specified, and the flight characteristics of aircraft can be accurately simulated. They are systems of formal logic that follow an unbroken chain from Parmenides, Heraclites, Plato, and Aristotle. Since every competent expert system must have some way of hedging when facts are unclear, it is safe to say that all useful expert systems will be at least partly fuzzy pretty soon.