COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Actively probing and modeling users in interactive coevolution
Proceedings of the 8th annual conference on Genetic and evolutionary computation
ALPS: the age-layered population structure for reducing the problem of premature convergence
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Computer
IEEE Transactions on Evolutionary Computation - Special issue on preference-based multiobjective evolutionary algorithms
Crowdsourcing systems on the World-Wide Web
Communications of the ACM
Preference-based policy learning
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
Preference-based policy iteration: leveraging preference learning for reinforcement learning
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
Picbreeder: A case study in collaborative evolutionary exploration of design space
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
Avoiding local optima with interactive evolutionary robotics
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
IEEE Transactions on Evolutionary Computation
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Methodologies are emerging in many branches of computer science that demonstrate how human users and automated algorithms can collaborate on a problem such that their combined solutions outperform those produced by either humans or algorithms alone. The problem of behavior optimization in robotics seems particularly well-suited for this approach because humans have intuitions about how animals---and thus robots---should and should not behave, and can visually detect non-optimal behaviors that are trapped in local optima. Here we introduce a multiobjective approach in which a surrogate user (which stands in for a human user) deflects search away from local optima and a traditional fitness function eventually leads search toward the global optimum. We show that this approach produces superior solutions for a deceptive robotics problem compared to a similar search method that is guided by just a surrogate user or just a fitness function.