Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Interactive estimation of agent-based financial markets models: modularity and learning
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Co-evolutionary Variance Can Guide Physical Testing in Evolutionary System Identification
EH '05 Proceedings of the 2005 NASA/DoD Conference on Evolvable Hardware
Predicting solution rank to improve performance
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Learning comparative user models for accelerating human-computer collaborative search
EvoMUSART'12 Proceedings of the First international conference on Evolutionary and Biologically Inspired Music, Sound, Art and Design
Proceedings of the 14th 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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A major challenge in interactive evolution is extracting user preferences with minimal probing. We introduce an interactive multi-objective coevolutionary algorithm that actively selects the most informative probes: We simultaneously coevolve a population of candidate models that explain users' selection so far, and a population of candidate probes that cause the most divergence among model predictions, thereby elucidating model uncertainties (divergence). As progress is made, we begin selecting for probes with the highest expected outcome averaged among different models, thereby exploiting model certainties (consensus). In the evolution of pen stroke drawings, we find this technique to be highly effective at extracting preference models from very limited human interaction. Using only pair-wise preference questions, strategy and preference in pen stroke drawings are extracted in fewer than ten user probes. Our results show that the optimal questions to probe the user need not include drawings similar to the target drawing. Instead, the user models converge on trends in the user responses, thereby extrapolating strong preference for target drawings which the models are never actually trained to prefer.