Collective intelligence of genetic programming for macroeconomic forecasting

Authors:
Jerzy Duda;Stanisław Szydło
Affiliations:
Department of Applied Computer Science, AGH University;Department of Economy, Finance and Environmental Management, AGH University of Science and Technology, Faculty of Management, Kraków, Poland
Venue:
ICCCI'11 Proceedings of the Third international conference on Computational collective intelligence: technologies and applications - Volume Part II
Year:
2011

Citing 6
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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.