Evolving neural networks through augmenting topologies
Evolutionary Computation
Combating user fatigue in iGAs: partial ordering, support vector machines, and synthetic fitness
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
A Theory of Fun for Game Design
A Theory of Fun for Game Design
Picbreeder: evolving pictures collaboratively online
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Evolving competitive car controllers for racing games with neuroevolution
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Robust player imitation using multiobjective evolution
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Evo'08 Proceedings of the 2008 conference on Applications of evolutionary computing
Towards a generic framework for automated video game level creation
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
Search-based procedural content generation
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
IEEE Transactions on Evolutionary Computation
Learning, evolution and adaptation in racing games
Proceedings of the 9th conference on Computing Frontiers
Digging deeper into platform game level design: session size and sequential features
EvoApplications'12 Proceedings of the 2012t European conference on Applications of Evolutionary Computation
3D game model and texture generation using interactive genetic algorithm
Proceedings of the Workshop at SIGGRAPH Asia
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Fast exact graph matching using adjacency matrices
Proceedings of the The third workshop on Procedural Content Generation in Games
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We present a framework for the procedural generation of tracks for a high-end car racing game (TORCS) using interactive evolution. The framework maintains multiple populations and allow users to work both on their own population (in single-user mode) or to collaborate with other users on a shared population. Our architecture comprises a web frontend and an evolutionary backend. The former manages the interaction with users (e.g., logs registered and anonymous users, collects evaluations, provides access to all the evolved populations) and maintains the database server that stores all the present/past populations. The latter runs all the tasks related to evolution (selection, recombination and mutation) and all the tasks related to the target racing game (e.g., the track generation). We performed two sets of experiments involving five human subjects to evolve racing tracks alone (in a single-user mode) or cooperatively. Our preliminary results on five human subjects show that, in all the experiments, there is an increase of users' satisfaction as the evolution proceeds. Users stated that they perceived improvements in the quality of the individuals between subsequent populations and that, at the end, the process produced interesting tracks.