A spatially-structured PCG method for content diversity in a Physics-based simulation game Ra´ ul Lara-Cabrera 1 , Alejandro Gutierrez-Alcoba 2 , and Antonio J. Fern´ andez-Leiva 1 1 Departmento de Lenguajes y Ciencias de la Computaci´ on, Universidad de M´ alaga {raul,afdez}@lcc.uma.es 2 Departamento de Arquitectura de Computadores, Universidad de M´ alaga agutierrez@ac.uma.es Abstract. This paper presents a spatially-structured evolutionary al- gorithm (EA) to procedurally generate game maps of different levels of difficulty to be solved, in Gravityvolve!, a physics-based simulation videogame that we have implemented and which is inspired by the n- body problem, a classical problem in the field of physics and mathe- matics. The proposal consists of a steady-state EA whose population is partitioned into three groups according to the difficulty of the generated content (hard, medium or easy) which can be easily adapted to handle the automatic creation of content of diverse nature in other games. In ad- dition, we present three fitness functions, based on multiple criteria (i.e:, intersections, gravitational acceleration and simulations), that were used experimentally to conduct the search process for creating a database of maps with different difficulty in Gravityvolve!. Keywords: Content creation, Evolutionary algorithms, physics-based game, human evaluation 1 Introduction and motivation The economic costs of producing a video game are very high: the development is a slow process that requires a large team of heterogeneous professionals who, in addition, are required to be highly qualified and specialized. Therefore, any improvement that is able to optimize both the time and resources required to create a video game is always welcome. According to a recent analysis published in [1], the field of computational in- telligence in video games is a vibrant, active field, which attracts new researchers each year and generates new publications. There has been a steady growth in the number of authors, which was accentuated mid-decade 2000–2010. Moreover, the number of publications per year from the community has been increasing since 2005, thus supporting the continued growth of the community. One of the most promising areas in this field is Procedural Content Gener- ation (PCG) which consists of generating game content through algorithms in- stead of creating it by hand, and refers to each component that makes up a video