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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"

Her name is Angelina: she runs on a heavy-duty Mac server and she's building some addictive computer games for you. Angelina (a tail-recursive acronym for "A Novel Game-Evolving Labrat I've Named ANGELINA") is a project in evolutionary computing by Michael Cook, a PhD candidate at Imperial College in the UK.

Abstract: "This paper discusses how to use design patterns in procedural level generation, with particular reference to the classic console game Super Mario Bros. In a previous paper, we analyzed the levels in this game to nd a set of recurring level design patterns, and discussed an implementation where levels were produced from concatenation of these patterns. In this paper, we instead propose using patterns as design objectives. An implementation of this based on evolutionary computation is presented. In this implementation, levels are represented as a set of vertical slices from the original game, and the tness function count the number of patterns found. Qualitative analysis of generated levels is performed in order to identify strengths and challenges of this method"

From Imperial College London, Computational Creativity Group. Abstract. We present initial results from ACCME,A Co-operative Co-evolutionary Metroidvania Engine, which uses co-operative co-evolution to automatically evolve simple platform games. We describe the system in detail and justify the use of co-operative co-evolution. We then address two fundamental questions about the use of this method in automated game design, both in terms of its ability to maximise fitness functions, and whether our choice of fitness function produces scores which correlate with player preference in the resulting games.