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Approximate and probabilistic algorithms for shading and rendering structured particle systems
SIGGRAPH '85 Proceedings of the 12th annual conference on Computer graphics and interactive techniques
Evolutionary Design by Computers with CDrom
Evolutionary Design by Computers with CDrom
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
A Fast, Flexible, Particle-System Model for Cloth Draping
IEEE Computer Graphics and Applications
Evolving Neural Control Systems
IEEE Expert: Intelligent Systems and Their Applications
Evolving neural networks through augmenting topologies
Evolutionary Computation
Solving Non-Markovian Control Tasks with Neuro-Evolution
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Two Applications of Gentic Algorithms to Component Design
Selected Papers from AISB Workshop on Evolutionary Computing
Particle systems—a technique for modeling a class of fuzzy objects
SIGGRAPH '83 Proceedings of the 10th annual conference on Computer graphics and interactive techniques
A 3D Modeling System for Creative Design
ICOIN '01 Proceedings of the The 15th International Conference on Information Networking
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Proceedings of the 2003 ACM SIGGRAPH/Eurographics symposium on Computer animation
Introduction to 3d Game Programming with Directx 9.0
Introduction to 3d Game Programming with Directx 9.0
Comparing evolutionary and temporal difference methods in a reinforcement learning domain
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Compositional pattern producing networks: A novel abstraction of development
Genetic Programming and Evolvable Machines
A comparison between cellular encoding and direct encoding for genetic neural networks
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Competitive coevolution through evolutionary complexification
Journal of Artificial Intelligence Research
Connectionist theory refinement: genetically searching the space of network topologies
Journal of Artificial Intelligence Research
Real-time neuroevolution in the NERO video game
IEEE Transactions on Evolutionary Computation
Evolving content in the galactic arms race video game
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
Simulation of implosion and transportation of ore in digital mine
Transactions on edutainment VI
An IGA-based design support system for realistic and practical fashion designs
Computer-Aided Design
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Interactive Evolutionary Computation (IEC) creates the intriguing possibility that a large variety of useful content can be produced quickly and easily for practical computer graphics and gaming applications. To show that IEC can produce such content, this paper applies IEC to particle system effects, which are the de facto method in computer graphics for generating fire, smoke, explosions, electricity, water, and many other special effects. While particle systems are capable of producing a broad array of effects, they require substantial mathematical and programming knowledge to produce. Therefore, efficient particle system generation tools are required for content developers to produce special effects in a timely manner. This paper details the design, representation, and animation of particle systems via two IEC tools called NEAT Particles and NEAT Projectiles. Both tools evolve artificial neural networks (ANN) with the NeuroEvolution of Augmenting Topologies (NEAT) method to control the behavior of particles. NEAT Particles evolves general-purpose particle effects, whereas NEAT Projectiles specializes in evolving particle weapon effects for video games. The primary advantage of this NEAT-based IEC approach is to decouple the creation of new effects from mathematics and programming, enabling content developers without programming knowledge to produce complex effects. Furthermore, it allows content designers to produce a broader range of effects than typical development tools. Finally, it acts as a concept generator, allowing content creators to interactively and efficiently explore the space of possible effects. Both NEAT Particles and NEAT Projectiles demonstrate how IEC can evolve useful content for graphical media and games, and are together a step toward the larger goal of automated content generation.