A Computational Model of Poetic Creativity with Neural Network as Measure of Adaptive Fitness

The prevailing metaphor used in the study of creative cognition is the metaphor of evolution (Gruber and Davis 1988). Naturally, AI experimenters have implemented algorithms based loosely or precisely on Darwinian models of information transfer and change over time in attempts to make machines more creative (Fogel and Owens 1966). Simulating an evolving systems model of creative expression raises the important technical issue of how to judge the fitness of the forms the system produces. The use of a neural network is an appropriate and fairly novel (see also Biles 1996 and Burton and Vladimirova, 1997) answer to the question of how a fuzzy and subjective measure can be captured and put to use. This technical paper proposes a plan for the design of a creative computational system of this kind, and documents a working prototype called Poevolve that embodies some of these features.