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Symbiosis, complexification and simplicity under GP

Published:07 July 2010Publication History

ABSTRACT

Models of Genetic Programming (GP) frequently reflect a neo-Darwinian view to evolution in which inheritance is based on a process of gradual refinement and the resulting solutions take the form of single monolithic programs. Conversely, introducing an explicitly symbiotic model of inheritance makes a divide-and-conquer metaphor for problem decomposition central to evolution. Benchmarking gradualist versus symbiotic models of evolution under a common evolutionary framework illustrates that not only does symbiosis result in more accurate solutions, but the solutions are also much simpler in terms of instruction and attribute count over a wide range of classification problem domains.

References

  1. S. Ali and K.A. Smith. On learning algorithm selection for classification. Applied Soft Computing, 6:119--138, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. E.B. Baum and I.Durdanovic. Toward code evolution by artificial economies. In Evolution as Computation, pages 314--332. Springer, 2002.Google ScholarGoogle ScholarCross RefCross Ref
  3. M. Brameier and W. Banzhaf. A comparison of linear genetic programming and neural networks if medical data mining. IEEE Transactions on Evolutionary Computation, 5(1):17--26. 2001. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. S. Brave. Evolving recursive programs for tree search. In P.J. Angeline and K.E. Kinnear, editors, Advances in Genetic Programming, volume 2, chapter 10, pages 203--220. MIT, 1996. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. J. M. Daida, C.S. Grasso, S. A. Stanhope, and S. J. Ross. Symbionticism and complex adaptive systems I: Implications of having symbiosis occur in nature. In Proceedings of the Annual Conference on Evolutionary Programming, pages 177--186. MIT Press, 1996.Google ScholarGoogle Scholar
  6. E. D. de Jong. A monolithic archive for Pareto-coevolution. Evolutionary Computation, 15(1):61--94, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. J. Doucette, P.Lichodzijewski, and M.I. Heywood. Evolving coevolutionary classifiers under large attribute spaces. In Genetic Programming Theory and Practice VII, pages 37--54. Springer, 2009.Google ScholarGoogle Scholar
  8. I. Guyon, S. Gunn, M. Nikravesh, and L. Zadeh, editors. Feature Extraction: Foundations and Applications, volume 207 of Studies in Fuzziness and Soft Computing. Springer, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. M.I. Heywood and P.Lichodzijewski. Symbiogenesis as a mechanism for building complex adaptive systems: A review. In EvoApplications, volume 6024 of LNCS, pages 51--60. 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. L. Huelsbergen. Learning recursive sequences via evolution of machine-language programs. In Proceedings of the Annual Conference on Genetic Programming. pages 186--194, 1997.Google ScholarGoogle Scholar
  11. J. R. Koza. Genetic Programming: On the programming of computers by means of natural selection MIT, 1992. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. U. Kutschera. Symbiogenesism natural selection, and the dynamic Earth. Theory in Biosciences, 128:191--203, 2009.Google ScholarGoogle ScholarCross RefCross Ref
  13. M. Lemczyk and M. I. Heywood. Training binary GP classifiers efficiently: A Pareto-coevolutionary approach. In European Conference on Genetic Programming, volume 4445 of LNCS, pages 229--240, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. P. Lichodzijewski and M. I. Heywood. GP classifier problem decomposition using first-price and second-price auctions. In European Conference on Genetic Programming, volume 4445 of LNCS, pages 137--147, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. P. Lichodzijewski and M. I. Heywood. Managing team-based problem solving with symbiotic bid-based genetic programming. In Proceedings of the Genetic and Evolutionary Computation Conference, pages 363--370, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Y. Liu, X.Yao, and T.Higuchi. Evolutionary Ensembles with negative correlation learning. IEEE Transactions on Evolutionary Computation, 4(4): 380--387. 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. L. Margulis and R.Fester, editors. Symbiosis as a Source of Evolutionary Innovation. MIT Press, 1991.Google ScholarGoogle Scholar
  18. J. Maynard Smith and E. Szathmary. The origins of life. Oxford University Press, 1999.Google ScholarGoogle Scholar
  19. A. R. McIntyre and M. I. Heywood. Pareto cooperative-competitive Genetic Programming: A classification benchmarking study. In Genetic Programming Theory and Practice VI, pages 43--60. Springer, 2008.Google ScholarGoogle Scholar
  20. D. E. Moriarty and R. Miikkulainen. Forming neural networks through efficient and adaptive coevolution. Evolutionary Computation, 5(4):373--399, 1998. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. L. Panait, S. Luke, and R. P. Wiegand. Biasing coevolutionary search for optimal multiagent behaviors. IEEE Transactions on Evolutionary Computation, 10(6):629--645. 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. M. Potter and K. De Jong. Cooperative coevolution: an architecture for evolving coadapted subcomponents. Evolutionary Computation, 8(1):1--29, 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. J. P. Rosca and D. H. Ballard. Hierarchical self-organization in genetic programming. In International Conference on Machine Learning, page. 251--258, 1994.Google ScholarGoogle ScholarCross RefCross Ref
  24. R. Thomason and T. Soule. Novel ways of improving cooperation and performance in ensemble classifiers. In Proceedings of the Genetic and Evolutionary Computation Conference, pages 1708--1715, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. S. van Dijk, D.Thierens, and M. de Berg. On the design and analysis of competent selecto-recominative GAs. Evolutionary Computation, 12(2): 243--267, 2004.. Google ScholarGoogle ScholarDigital LibraryDigital Library

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          cover image ACM Conferences
          GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation
          July 2010
          1520 pages
          ISBN:9781450300728
          DOI:10.1145/1830483

          Copyright © 2010 ACM

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          • Published: 7 July 2010

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