Abstract
A collective approach to the problem of developing forecasts for macroeconomic indicators is presented in the paper. The main advantage of genetic programming over artificial neural networks is that it generates human readable mathematical expressions that can be interpreted by a decision-maker. Gene expression programming used in the paper is an example of collective adaptive system, but we propose to use a collective intelligence to develop not only one forecasting model, but a set of models, from which the most suitable one can be chosen automatically or manually by the decision-maker.
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Duda, J., Szydło, S. (2011). Collective Intelligence of Genetic Programming for Macroeconomic Forecasting. In: Jędrzejowicz, P., Nguyen, N.T., Hoang, K. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2011. Lecture Notes in Computer Science(), vol 6923. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23938-0_45
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DOI: https://doi.org/10.1007/978-3-642-23938-0_45
Publisher Name: Springer, Berlin, Heidelberg
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