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Many seemingly different problems in artificial intelligence, symbolic processing, and machine learning can be viewed as requiring 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 most fit individual computer program. The new "genetic programming" paradigm described herein provides a way to search for this most fit individual computer program. In this new "genetic programming" paradigm, populations of computer programs are genetically bred using the Darwinian principle of survival of the fittest and using a genetic crossover (recombination) operator appropriate for genetically mating computer programs. In this paper, the process of formulating and solving problems using this new paradigm is illustrated using examples from various areas. Examples come from the areas of machine learning of a function; planning; sequence induction; function function identification (including symbolic regression, empirical discovery, "data to function" symbolic integration, "data to function" symbolic differentiation); solving equations, including differential equations, integral equations, and functional equations); concept formation; automatic programming; pattern recognition, time-optimal control; playing differential pursuer-evader games; neural network design; and finding a game-playing strategyfor a discrete game in extensive form.