Evolution of a fictional dialogue

This paper describes user-supervised Evolutionary Algorithm (EA) experiments that investigate the evolution of a sensible fictional dialogue. A user-supervised EA was used given the difficulty of defining a fitness function for evolving art tasks. Two EAs were tested for the task of evolving dialogue given an English word population. The EAs required user-assigned fitness values to be given as input with varying degrees of frequency during the evolutionary process. The success of the EAs were comparatively evaluated with respect to two-point recombination and a novel complement gene scan operator. Task performance was evaluated according to average fitness, word and genotype diversity, and the number of words used in the fittest evolved dialogue. Results indicated that for both EAs, complement gene scan was more effective for evolving complex, sensible and grammatically correct dialogue, comparative to sentences evolved by the EAs using two-point recombination.