19 August 1993 Evolving recurrent perceptrons
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Abstract
This woit investigates the application of evolutionary programming, a multi-agent stochastic search technique, to the generation of recurrent perceptrons (nonlinear hR filters) for time-series prediction tasks. The evolutionary programming paradigm is discussed and analogies are made to classical stochastic optimization methods. A hybrid optimization scheme is proposed based on multi-agent and single-agent random optimization techniques. This method is then used to determine both the model order and weight coefficients of linear, nonlinear, and parallel linear-nonlinear nextstep predictors. The AIC is used as the cost function to score each candidate solution.
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John R. McDonnell, Donald E. Waagen, "Evolving recurrent perceptrons", Proc. SPIE 1966, Science of Artificial Neural Networks II, (19 August 1993); doi: 10.1117/12.152634; https://doi.org/10.1117/12.152634
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