Genetic and evolutionary algorithms come of age
Communications of the ACM
Evolutionary computation: toward a new philosophy of machine intelligence
Evolutionary computation: toward a new philosophy of machine intelligence
Evolutionary Computation: The Fossil Record
Evolutionary Computation: The Fossil Record
Evolutionary computation: comments on the history and current state
IEEE Transactions on Evolutionary Computation
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This paper presents a systems identification method, for discrete time linear systems, based on an evolutionary approach, which allows achieving the selection of a suitable structure and the parameters estimation, using non conventional objective functions. This algorithm incorporates parametric crossover and parametric mutation along a weighted gradient direction [1]. The performance of the proposed method is illustrated with computer simulations using ARX model structures, where parameters, model dynamical order and input-output delay values are estimated.