Nonlinear black-box modeling in system identification: a unified overview
Automatica (Journal of IFAC) - Special issue on trends in system identification
System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
Proceedings of the 2002 ACM symposium on Applied computing
Genetic Programming III: Darwinian Invention & Problem Solving
Genetic Programming III: Darwinian Invention & Problem Solving
Tracking the Red Queen: Measurements of Adaptive Progress in Co-Evolutionary Simulations
Proceedings of the Third European Conference on Advances in Artificial Life
Ideal Evaluation from Coevolution
Evolutionary Computation
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
New methods for competitive coevolution
Evolutionary Computation
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
Coevolution of simulator proxies and sampling strategies for petroleum reservoir modeling
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
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In previous papers we have described a co-evolutionary algorithm (EEA), the estimation-exploration algorithm, that infers the hidden inner structure of systems using minimal testing. In this paper we introduce the concept of 'managed challenge' to alleviate the problem of disengagement in this and other co-evol-utionary algorithms. A known problem in co-evolutionary dynamics occurs when one population systematically outperforms the other, resulting in a loss of selection pressure for both populations. In system identification (which deals with determining the inner structure of a system using only input/output data), multiple trials (a test that causes the system to produce some output) on the system to be identified must be performed. When such trials are costly, this disengagement results in wasted data that is not utilized by the evolutionary process. Here we propose that data from futile interactions should be stored during disengagement and automatically re-introduced later, when the population re-engages: we refer to this as the test bank. We demonstrate that the advantage of the test bank is two-fold: it allows for the discovery of more accurate models, and it reduces the amount of required training data for both parametric identification -- parameterizing inner structure -- and symbolic identification -- approximating inner structure using symbolic equations -- of nonlinear systems.