In nature, biological structures that are more successful in grappling with their environment survive and reproduce at a higher rate. Biologists interpret the structures they observe in nature as the consequence of Darwinian natural selection operating in an environment over a period of time. In other words, in nature, structure is the consequence of fitness. Fitness causes, over a period of time, the creation of structure via natural selection and the creative effects of sexual recombination (genetic crossover) and mutation. That is, fitness begets structure.
Computer programs are among the most complex structures created by man. The purpose of this book is to apply the notion that structure arises from fitness to one of the central questions in computer science (attributed to Arthur Samuel in the 1950s):
How can computers learn to solve problems without being explicitly programmed? In other words, how can computers be made to do what is needed to be done, without being told exactly how to do it?
One impediment to getting computers to solve problems without being explicitly programmed is that existing methods of machine learning, artificial intelligence, self-improving systems, self-organizing systems, neural networks, and induction do not seek solutions in the form of computer programs. Instead, existing paradigms involve specialized structures which are nothing like computer programs (e.g., weight vectors for neural networks, decision trees, formal grammars, frames, conceptual clusters, coefficients for polynomials, production rules, chromosome strings in the conventional genetic algorithm, and concept sets). Each of these specialized structures can facilitate the solution of certain problems, and many of them facilitate mathematical analysis that might not otherwise be possible. However, these specialized structures are an unnatural and constraining way of getting computers to solve problems without being explicitly programmed. Human programmers do not regard these specialized structures as having the flexibility necessary for programming computers, as evidenced by the fact that computers are not commonly programmed in the language of weight vectors, decision trees, formal grammars, frames, schemata, conceptual clusters, polynomial coefficients, production rules, chromosome strings, or concept sets.