The effects of interaction frequency on the optimization performance of cooperative coevolution
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Active Coevolutionary Learning of Deterministic Finite Automata
The Journal of Machine Learning Research
Action-selection and crossover strategies for self-modeling machines
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Exploiting multiple robots to accelerate self-modeling
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Informative sampling for large unbalanced data sets
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Indirect Online Evolution --- A Conceptual Framework for Adaptation in Industrial Robotic Systems
ICES '08 Proceedings of the 8th international conference on Evolvable Systems: From Biology to Hardware
Matlab software for inversion of describing functions
Advances in Engineering Software
Combined structure and motion extraction from visual data using evolutionary active learning
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Anticipatory Behavior in Adaptive Learning Systems
Accelerating self-modeling in cooperative robot teams
IEEE Transactions on Evolutionary Computation
Automatic system identification based on coevolution of models and tests
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Coevolution of simulator proxies and sampling strategies for petroleum reservoir modeling
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Synthesizing physically-realistic environmental models from robot exploration
ECAL'07 Proceedings of the 9th European conference on Advances in artificial life
50 years of artificial intelligence
Predicting solution rank to improve performance
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Efficient multi-objective higher order mutation testing with genetic programming
Journal of Systems and Software
Incrementally discovering testable specifications from program executions
FMCO'09 Proceedings of the 8th international conference on Formal methods for components and objects
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
A comparison of sampling strategies for parameter estimation of a robot simulator
SIMPAR'12 Proceedings of the Third international conference on Simulation, Modeling, and Programming for Autonomous Robots
A coevolutionary approach to learn animal behavior through controlled interaction
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Design for a darwinian brain: part 2. cognitive architecture
Living Machines'13 Proceedings of the Second international conference on Biomimetic and Biohybrid Systems
Fast damage recovery in robotics with the T-resilience algorithm
International Journal of Robotics Research
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We present a coevolutionary algorithm for inferring the topology and parameters of a wide range of hidden nonlinear systems with a minimum of experimentation on the target system. The algorithm synthesizes an explicit model directly from the observed data produced by intelligently generated tests. The algorithm is composed of two coevolving populations. One population evolves candidate models that estimate the structure of the hidden system. The second population evolves informative tests that either extract new information from the hidden system or elicit desirable behavior from it. The fitness of candidate models is their ability to explain behavior of the target system observed in response to all tests carried out so far; the fitness of candidate tests is their ability to make the models disagree in their predictions. We demonstrate the generality of this estimation-exploration algorithm by applying it to four different problems—grammar induction, gene network inference, evolutionary robotics, and robot damage recovery—and discuss how it overcomes several of the pathologies commonly found in other coevolutionary algorithms. We show that the algorithm is able to successfully infer and/or manipulate highly nonlinear hidden systems using very few tests, and that the benefit of this approach increases as the hidden systems possess more degrees of freedom, or become more biased or unobservable. The algorithm provides a systematic method for posing synthesis or analysis tasks to a coevolutionary system.