Controllable procedural map generation via multiobjective evolution

Julian Togelius, Mike Preuss, Nicola Beume, Simon Wessing, Johan Hagelbäck, Georgios N. Yannakakis, Corrado Grappiolo

    Research output: Contribution to journalArticlepeer-review


    This paper shows how multiobjective evolutionary algorithms can be used to procedurally generate complete and playable maps for real-time strategy (RTS) games. We devise heuristic objective functions that measure properties of maps that impact important aspects of gameplay experience. To show the generality of our approach, we design two different evolvable map representations, one for an imaginary generic strategy game based on heightmaps, and one for the classic RTS game StarCraft. The effect of combining tuples or triples of the objective functions are investigated in systematic experiments, in particular which of the objectives are partially conflicting. A selection of generated maps are visually evaluated by a population of skilled StarCraft players, confirming that most of our objectives correspond to perceived gameplay qualities. Our method could be used to completely automate in-game controlled map generation, enabling player-adaptive games, or as a design support tool for human designers.

    Original languageEnglish (US)
    Pages (from-to)245-277
    Number of pages33
    JournalGenetic Programming and Evolvable Machines
    Issue number2
    StatePublished - Jun 2013


    • Evolutionary computation
    • Multiobjective optimisation
    • Procedural content generation
    • RTS
    • Real-time strategy games
    • StarCraft

    ASJC Scopus subject areas

    • Software
    • Theoretical Computer Science
    • Hardware and Architecture
    • Computer Science Applications


    Dive into the research topics of 'Controllable procedural map generation via multiobjective evolution'. Together they form a unique fingerprint.

    Cite this