FIFTH™: a stack based GP language for vector processing

Authors:
Kenneth Holladay;Kay Robbins;Jeffery Von Ronne
Affiliations:
Southwest Research Institute, San Antonio, Texas;University of Texas at San Antonio, San Antonio, Texas;University of Texas at San Antonio, San Antonio, Texas
Venue:
EuroGP'07 Proceedings of the 10th European conference on Genetic programming
Year:
2007

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Abstract

FIFTH™, a new stack-based genetic programming language, efficiently expresses solutions to a large class of feature recognition problems. This problem class includes mining time-series data, classification of multivariate data, image segmentation, and digital signal processing (DSP). FIFTH is based on FORTH principles. Key features of FIFTH are a single data stack for all data types and support for vectors and matrices as single stack elements. We demonstrate that the language characteristics allow simple and elegant representation of signal processing algorithms while maintaining the rules necessary to automatically evolve stack correct and control flow correct programs. FIFTH supports all essential program architecture constructs such as automatically defined functions, loops, branches, and variable storage. An XML configuration file provides easy selection from a rich set of operators, including domain specific functions such as the Fourier transform (FFT). The fully-distributed FIFTH environment (GPE5) uses CORBA for its underlying process communication.