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Strengths and weaknesses of FSA representation

Published: 07 July 2007 Publication History

Abstract

Genetic Programming and Evolutionary Programming are fields studying the application of artificial evolutionon evolving directly executable programs, in form of trees similar to Lisp expressions (GP-trees), or Finite State Automata (FSA).In this exercise, we study the performance of these methods on several example problems, and draw conclusionson the suitability of the representations with respect to the task structure and properties. We investigate the roleof incremental evolution and its bias in the context of FSA representation. The experiments are performed in simulation and/or confirmed on real robots.

References

[1]
W. Banzhaf, P. Nordin, R. E. Keller, and F. D. Francone. Genetic Programming, An Introduction. Morgan Kaufmann Publishers, Inc., 1998.
[2]
P. Petrovic. Evolving automatons for distributed behavior arbitration. Technical Report IDI 05/05, Norwegian University of Science and Technology, 2005.
[3]
P. Petrovic. Comparing finite-state automata representation with gp-trees. Technical Report IDI 05/06, 2006.

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cover image ACM Conferences
GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
July 2007
2313 pages
ISBN:9781595936974
DOI:10.1145/1276958

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 July 2007

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  1. evolutionary programming
  2. finite state automata
  3. incremental evolution

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GECCO '07 Paper Acceptance Rate 266 of 577 submissions, 46%;
Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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