NEAT Particles: Design, Representation, and Animation of Particle System Effects

Abstract

Particle systems are a representation, computation, and rendering method for special effects such as fire, smoke, explosions, electricity, water, magic, and many other phenomena. This paper presents NEAT particles, a new design, representation, and animation method for particle systems tailored to real-time effects in video games and simulations. In NEAT particles, the neuroevolution of augmenting topologies (NEAT) method evolves artificial neural networks (ANN) that control the appearance and motion of particles. NEAT particles affords three primary advantages over traditional particle effect development methods. First, it decouples the creation of new particle effects from mathematics and programming, enabling users with little knowledge of either to produce complex effects. Second, it allows content designers to evolve a broader range of effects than typical development tools through a form of interactive evolutionary computation (IEC). And finally, it acts as a concept generator, allowing users to interactively explore the space of possible effects. In the future such a system may allow content to be evolved in the game itself, as it is played