Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Principles of data mining
Numerical Recipes in C++: the art of scientific computing
Numerical Recipes in C++: the art of scientific computing
Genetic Programming and Evolvable Machines
Genetic Programming for Mining DNA Chip Data from Cancer Patients
Genetic Programming and Evolvable Machines
Genetic Programming and Evolvable Machines
Knowledge mining with genetic programming methods for variable selection in flavor design
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Two fast tree-creation algorithms for genetic programming
IEEE Transactions on Evolutionary Computation
On the architecture and implementation of tree-based genetic programming in HeuristicLab
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
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Macro-economic models describe the dynamics of economic quantities. The estimations and forecasts produced by such models play a substantial role for financial and political decisions. In this contribution we describe an approach based on genetic programming and symbolic regression to identify variable interactions in large datasets. In the proposed approach multiple symbolic regression runs are executed for each variable of the dataset to find potentially interesting models. The result is a variable interaction network that describes which variables are most relevant for the approximation of each variable of the dataset. This approach is applied to a macro-economic dataset with monthly observations of important economic indicators in order to identify potentially interesting dependencies of these indicators. The resulting interaction network of macro-economic indicators is briefly discussed and two of the identified models are presented in detail. The two models approximate the help wanted index and the CPI inflation in the US.