Artificial evolution for computer graphics
Proceedings of the 18th annual conference on Computer graphics and interactive techniques
Actively probing and modeling users in interactive coevolution
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Aesthetic evolutionary algorithm for fractal-based user-centered jewelry design
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Picbreeder: A case study in collaborative evolutionary exploration of design space
Evolutionary Computation
Nonlinear System Identification Using Coevolution of Models and Tests
IEEE Transactions on Evolutionary Computation
Aesthetic selection and the stochastic basis of art, design and interactive evolutionary computation
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Combining fitness-based search and user modeling in evolutionary robotics
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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Interactive Evolutionary Algorithms (IEAs) have much potential for allowing a human user to guide a search algorithm, but have struggled to overcome the limitations of slow, easily-fatigued human users. Here we describe The Approximate User (TAU) system in which these limitations are overcome by using a model of the user's preferences - which are continuously built and refined during the search process - to drive the search algorithm. Two variations of a user-modeling approach are compared to determine if this approach can accelerate IEA search. The two user-modeling approaches compared are: 1. learning a classifier which correctly determines which of two designs is better; and 2. learning a model which predicts a fitness score. Rather than having people do the user-testing, we propose the use of a simulated user as an easier means to test IEAs. Both variants of the TAU IEA are compared against a basic IEA and it is shown that TAU is up to 2.7 times faster and 15 times more reliable at producing near optimal results. In addition, we see TAU as a step toward building a more general Human-Computer Collaborative system.