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
Combining fitness-based search and user modeling in evolutionary robotics
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Learning aesthetic judgements in evolutionary art systems
Genetic Programming and Evolvable Machines
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Interactive Evolutionary Algorithms (IEAs) are a powerful explorative search technique that utilizes human input to make subjective decisions on potential problem solutions. But humans are slow and get bored and tired easily, limiting the usefulness of IEAs. Here we describe our system which works toward overcoming these problems, The Approximate User (TAU), and also a simulated user as a means to test IEAs. With TAU, as the user interacts with the IEA a model of the user's preferences is constructed and continually refined and this model is what is used as the fitness function to drive evolutionary search. The resulting system is a step toward our longer term goal of building a human-computer collaborative search system. In comparing the TAU IEA against a basic IEA it is found that TAU is 2.5 times faster and 15 times more reliable at producing near optimal results.