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The genetic programming paradigm: Genetically breeding populations of computer programs to solve problems
Dynamic, Genetic, and Chaotic Programming, John Wiley, New York, (1992)
Аннотация
Many seemingly different problems in machine learning,
artificial intelligence, and symbolic processing can be
viewed as requiring the discovery of a computer program
that produces some desired output for particular
inputs. When viewed in this way, the process of solving
these problems becomes equivalent to searching a space
of possible computer programs for a highly fit
individual computer program. The recently developed
genetic programming paradigm described herein provides
a way to search the space of possible computer programs
for a highly fit individual computer program to solve
(or approximately solve) a surprising variety of
different problems from different fields. In the
genetic programming paradigm, populations of computer
programs are genetically bred using the Darwinian
principle of survival of the fittest and using a
genetic crossover (sexual recombination) operator
appropriate for genetically mating computer programs.
This chapter shows how to reformulate seemingly
different problems into a common form (i.e. a problem
requiring discovery of a computer program) and, then,
to show how the genetic programming paradigm can serve
as a single, unified approach for solving problems
formulated in this common way.
