# Language Representation Progression in Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{teller-FSS-GP,
author =       "Astro Teller",
title =        "Language Representation Progression in Genetic
Programming",
booktitle =    "Working Notes for the AAAI Symposium on Genetic
Programming",
year =         "1995",
editor =       "E. V. Siegel and J. R. Koza",
pages =        "106--113",
address =      "MIT, Cambridge, MA, USA",
publisher_address = "445 Burgess Drive, Menlo Park, CA 94025, USA",
month =        "10--12 " # nov,
publisher =    "AAAI",
keywords =     "genetic algorithms, genetic programming, memory",
URL =          "http://www.aaai.org/Papers/Symposia/Fall/1995/FS-95-01/FS95-01-015.pdf",
URL =          "http://www.aaai.org/Library/Symposia/Fall/fs95-01.php",
size =         "8 pages",
abstract =     "The signal-to-symbol problem is the task of converting
raw sensor data into a set of symbols that Artificial
Intelligence systems can reason about. We have
developed a method for directly learning and combining
algorithms that map signals into symbols. This new
method is based on Genetic Programming (GP). Previous
papers have focused on PADO, our learning architecture.
We showed how PADO applies to the general
signal-to-symbol task and in particular the positive
results it brings to natural image object recognition.
Originally, PADO's programs were written in a Lisp-like
are now written in a very different language. Using
this new language, PADO's performance has increased
substantially on several domains including two vision
domains this paper will mention. This paper will
discuss these two language representations, the results
they produced, and some analysis of the performance
improvement. The higher level goals of this paper are
to give some justification for PADO's specific language
progression, some explanation for the improved
performance this progression generated, and to offer