Co-evolving parasites improve simulated evolution as an optimization procedure
CNLS '89 Proceedings of the ninth annual international conference of the Center for Nonlinear Studies on Self-organizing, Collective, and Cooperative Phenomena in Natural and Artificial Computing Networks on Emergent computation
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Evolving visually guided robots
Proceedings of the second international conference on From animals to animats 2 : simulation of adaptive behavior: simulation of adaptive behavior
Evolutionary robotics and the radical envelope-of-noise hypothesis
Adaptive Behavior
System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
Hardware Solutions for Evolutionary Robotics
Proceedings of the First European Workshop on Evolutionary Robotics
Online Choice of Active Learning Algorithms
The Journal of Machine Learning Research
'Managed challenge' alleviates disengagement in co-evolutionary system identification
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Ideal Evaluation from Coevolution
Evolutionary Computation
Active Coevolutionary Learning of Deterministic Finite Automata
The Journal of Machine Learning Research
Nonlinear System Identification Using Coevolution of Models and Tests
IEEE Transactions on Evolutionary Computation
Exploiting multiple robots to accelerate self-modeling
Proceedings of the 9th annual conference on Genetic and evolutionary computation
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
Fast damage recovery in robotics with the T-resilience algorithm
International Journal of Robotics Research
| Hi-index | 0.00 |
In previous work [7] a computational framework was demonstrated that employs evolutionary algorithms to automatically model a given system. This is accomplished by alternating the evolution of models with the evolutionary search for new training data. Theory predicts [23] that the best new training data is that which induces maximum disagreement across the current model set. Here it is demonstrated that in a robot application this is not the case, and alternative fitness functions are developed that seek other, better training data. Also, it is shown that although crossover successfully reduces the mean error of the model set, it compromises the ability of the framework to find new, informative training data. This has implications for how to create adaptive, self-modeling machines, and suggests how competitive processes in the brain underlie the generation of intelligent behavior.