Strengths and weaknesses of FSA representation

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Strengths and weaknesses of FSA representation

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Genetic And Evolutionary Computation Conference archive
Proceedings of the 9th annual conference on Genetic and evolutionary computation table of contents

London, England

POSTER SESSION: Evolution strategies, evolutionary programming: posters table of contents

Pages: 723 - 725

Year of Publication: 2007

ISBN:978-1-59593-697-4

Author

Pavel Petrovic

NTNU, Trondheim, Norway

Sponsors

SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation
ACM: Association for Computing Machinery

Publisher

ACM New York, NY, USA

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Downloads (6 Weeks): 2, Downloads (12 Months): 22, Citation Count: 0

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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

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	Wolfgang Banzhaf , Frank D. Francone , Robert E. Keller , Peter Nordin, Genetic programming: an introduction: on the automatic evolution of computer programs and its applications, Morgan Kaufmann Publishers Inc., San Francisco, CA, 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.