Symbolic regression using abstract expression grammars

Authors:
Michael F. Korns
Affiliations:
Freeman Investment Management, Henderson, NV, USA
Venue:
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Year:
2009

Citing 3
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Abstract

Abstract Expression Grammars have the potential to integrate Genetic Algorithms, Genetic Programming, Swarm Intelligence, and Differential Evolution into a seamlessly unified array of tools for use in symbolic regression. The features of abstract expression grammars are explored, examples of implementations are provided, and the beneficial effects of abstract expression grammars are tested with several published nonlinear regression problems.