Abstract: "This paper introduces
Rank-based Interactive Evo-
lution
(RIE) which is an alternative to interactive evolution
driven by computational models of user preferences to generate
personalized content. In RIE, the computational models are
adapted to the preferences of users which, in turn, are used as
fitness functions for the optimization of the generated content.
The preference models are built via ranking-based preference
learning, while the content is generated via evolutionary search.
The proposed method is evaluated on the creation of strategy
game maps, and its performance is tested using artificial agents.
Results suggest that RIE is both faster and more robust than
standard interactive evolution and outperforms other state-of-
the-art interactive evolution approaches"