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Solving the artificial ant on the Santa Fe trail problem in 20,696 fitness evaluations

Published: 07 July 2007 Publication History

Abstract

In this paper, we provide an algorithm that systematically considers all small trees in the search space of genetic programming. These small trees are used to generate useful subroutines for genetic programming. This algorithm is tested on the Artificial Ant on the Santa Fe Trail problem, a venerable problem for genetic programming systems. When four levels of iteration are used, the algorithm presented here generates better results than any known published result by a factor of 7.

References

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Koza, J.R. Genetic Programming II: Automatic Discovery of Reusable Programs. MIT Press, Cambridge, USA. 1994.
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Koza, J.R. Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, USA. 1992.
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Langdon, W.B., and Poli R., Why Ants Are Hard. Genetic Programming 1998: Proceedings of the Third Annual Conference. 193--201. Morgan Kaufmann, Madison, USA. 1998.
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Chellapilla, K. Evolutionary Programming with Tree Mutations: Evolving Computer Programs Without Crossover. In Koza, J.R. et al., eds., Genetic Programming 1997: Proceedings of the Second Annual Conference. Morgan Kaufmann. 1997.
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Luke, S. and Panait, L. A Survey and Comparison of Tree Generation Algorithms. In Spector, L., Goodman, E.D., Wu, A., Langdon, W.B., Voigt, H.-M., Gen, M., Sen, S., Dorigo, M., Pezeshk, S., Garzon, M.H., Burke, E., eds. Proceedings of the 2001 Genetic and Evolutionary Computation Conference. 81--88. Morgan Kaufmann, San Francisco, USA. 2001.
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Iba, H. Random Tree Generation for Genetic Programming. In Voigt H.-M., Ebeling W., Rechenberg I., Schwefel, eds. Parallel Problem Solving From Nature IV, Proceedings of the International Conference on Evolutionary Computation, LNCS 1141. 144--153. Springer Verlag, Berlin, Germany. 1996.
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Christensen S., Towards Scalable Genetic Programming, Ph.D. Thesis. 122--132. Ottawa-Carleton Institute for Computer Science, Ottawa, Canada. 2007.
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ECJ: A Java-based Evolutionary Computation and Genetic Programming System. Available at http://cs.gmu.edu/~eclab/projects/ecj/. 2007.
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Langdon W.B., and Poli, R. Fitness Causes Bloat: Mutation. In Banzhaf W., et al. eds. Proceedings of the First European Workshop on Genetic Programming. Springer-Verlag. 1998.
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Christensen, S., and Oppacher, F. An Analysis of Koza's Computational Effort Statistic for Genetic Programming. In Foster, J.A., Lutton, E., Miller, J.F., Ryan, C., Tettamanzi, A., eds. Genetic Programming, 5th European Conference, EuroGP 2002. LNCS 2278. 182--191. Springer-Verlag, Heidelberg, Germany. 2002.
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  • (2015)Sequential Symbolic Regression with Genetic ProgrammingGenetic Programming Theory and Practice XII10.1007/978-3-319-16030-6_5(73-90)Online publication date: 5-Jun-2015
  • (2013)Solving Five Instances of the Artificial Ant Problem with Ant Colony OptimizationIFAC Proceedings Volumes10.3182/20130619-3-RU-3018.0043646:9(1043-1048)Online publication date: 2013
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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
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 07 July 2007

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Author Tags

  1. genetic programming
  2. representations
  3. running time analysis
  4. speedup technique

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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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Cited By

View all
  • (2020)Solving artificial ant problem using two artificial bee colony programming versionsApplied Intelligence10.1007/s10489-020-01741-0Online publication date: 25-Jun-2020
  • (2015)Sequential Symbolic Regression with Genetic ProgrammingGenetic Programming Theory and Practice XII10.1007/978-3-319-16030-6_5(73-90)Online publication date: 5-Jun-2015
  • (2013)Solving Five Instances of the Artificial Ant Problem with Ant Colony OptimizationIFAC Proceedings Volumes10.3182/20130619-3-RU-3018.0043646:9(1043-1048)Online publication date: 2013
  • (2013)MuACOsmProceedings of the 15th annual conference on Genetic and evolutionary computation10.1145/2463372.2463440(511-518)Online publication date: 6-Jul-2013
  • (2013)Automated problem decomposition for the boolean domain with genetic programmingProceedings of the 16th European conference on Genetic Programming10.1007/978-3-642-37207-0_15(169-180)Online publication date: 3-Apr-2013
  • (2010)Efficiently evolving programs through the search for noveltyProceedings of the 12th annual conference on Genetic and evolutionary computation10.1145/1830483.1830638(837-844)Online publication date: 7-Jul-2010
  • (2009)HS-ModelProceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation10.1145/1543834.1543994(1005-1008)Online publication date: 12-Jun-2009
  • (2008)A survey and taxonomy of performance improvement of canonical genetic programmingKnowledge and Information Systems10.1007/s10115-008-0184-921:1(1-39)Online publication date: 12-Dec-2008

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