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Automated Problem Decomposition for the Boolean Domain with Genetic Programming

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Genetic Programming (EuroGP 2013)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7831))

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Abstract

Researchers have been interested in exploring the regularities and modularity of the problem space in genetic programming (GP) with the aim of decomposing the original problem into several smaller subproblems. The main motivation is to allow GP to deal with more complex problems. Most previous works on modularity in GP emphasise the structure of modules used to encapsulate code and/or promote code reuse, instead of in the decomposition of the original problem. In this paper we propose a problem decomposition strategy that allows the use of a GP search to find solutions for subproblems and combine the individual solutions into the complete solution to the problem.

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References

  1. Angeline, P.J., Pollack, J.B.: The evolutionary induction of subroutines. In: Proc. of the 14th Annual Conference of the Cognitive Science Society, pp. 236–241 (1992)

    Google Scholar 

  2. Angeline, P.J., Pollack, J.B.: Coevolving High-level Representations. In: Langton, C. (ed.) Artificial Life III, pp. 55–71. Addison-Wesley (1994), http://www.isrl.uiuc.edu/~amag/langev/paper/angeline94coevolvingHigh.html

  3. Christensen, S., Oppacher, F.: Solving the Artificial Ant on the Santa Fe Trail Problem in 20,696 Fitness Evaluations. In: Proc. of GECCO, pp. 1574–1579 (2007)

    Google Scholar 

  4. Hemberg, E., Gilligan, C., O’Neill, M., Brabazon, A.: A Grammatical Genetic Programming Approach to Modularity in Genetic Algorithms. In: Ebner, M., O’Neill, M., Ekárt, A., Vanneschi, L., Esparcia-Alcázar, A.I. (eds.) EuroGP 2007. LNCS, vol. 4445, pp. 1–11. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  5. Jackson, D., Gibbons, A.P.: Layered Learning in Boolean GP Problems. In: Ebner, M., O’Neill, M., Ekárt, A., Vanneschi, L., Esparcia-Alcázar, A.I. (eds.) EuroGP 2007. LNCS, vol. 4445, pp. 148–159. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  6. Keijzer, M., Ryan, C., Cattolico, M.: Run Transferable Libraries — Learning Functional Bias in Problem Domains. In: Deb, K., Tari, Z. (eds.) GECCO 2004. LNCS, vol. 3103, pp. 531–542. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  7. Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press (1992)

    Google Scholar 

  8. Koza, J.R.: Genetic Programming II: Automatic Discovery of Reusable Programs. MIT Press (1994)

    Google Scholar 

  9. Koza, J.R., Bennett III, F.H., Andre, D., Keane, M.: Genetic Programming III: Darwinian Invention and Problem Solving. Morgan Kaufmann (1999)

    Google Scholar 

  10. Luke, S.: ECJ: A Java-based Evolutionary Computation Research System (2012), http://cs.gmu.edu/~eclab/projects/ecj/

  11. McKay, R.: Partial Functions in Fitness-Shared Genetic Programming. In: Proc. of CEC, pp. 349–356 (2000)

    Google Scholar 

  12. Moraglio, A., Krawiec, K., Johnson, C.G.: Geometric Semantic Genetic Programming. In: Coello, C.A.C., Cutello, V., Deb, K., Forrest, S., Nicosia, G., Pavone, M. (eds.) PPSN 2012, Part I. LNCS, vol. 7491, pp. 21–31. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  13. O’Neill, M., Vanneschi, L., Gustafson, S., Banzhaf, W.: Open issues in genetic programming. Genetic Programming and Evolvable Machines 11, 339–363 (2010)

    Article  Google Scholar 

  14. Otero, F., Castle, T., Johnson, C.: EpochX: Genetic Programming in Java with Statistics and Event Monitoring. In: Proc. GECCO Companion, pp. 93–100 (2012)

    Google Scholar 

  15. Roberts, S.C., Howard, D., Koza, J.R.: Evolving Modules in Genetic Programming by Subtree Encapsulation. In: Miller, J., Tomassini, M., Lanzi, P.L., Ryan, C., Tetamanzi, A.G.B., Langdon, W.B. (eds.) EuroGP 2001. LNCS, vol. 2038, pp. 160–175. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  16. Rosca, J., Ballard, D.: Learning by adapting representations in genetic programming. In: Proc. of the IEEE WCCI, pp. 407–412 (1994)

    Google Scholar 

  17. Spector, L., Martin, B., Harrington, K., Helmuth, T.: Tag-Based Modules in Genetic Programming. In: Proc. of GECCO, pp. 1419–1426 (2011)

    Google Scholar 

  18. Swafford, J., Hemberg, E., O’Neill, M., Nicolau, M., Brabazon, A.: A Non-Destructive Grammar Modification Approach to Modularity in Grammatical Evolution. In: Proc. GECCO, pp. 1411–1418 (2011)

    Google Scholar 

  19. Walker, J., Miller, J.: The automatic acquisition, evolution and reuse of modules in cartesian genetic programming. IEEE Transactions on Evolutionary Computation 12(4), 397–417 (2008)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. School of Computing, University of Kent, Canterbury, UK

    Fernando E. B. Otero & Colin G. Johnson

Authors
  1. Fernando E. B. Otero
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  2. Colin G. Johnson
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Editor information

Editors and Affiliations

  1. Institute of Computing Science, Poznan University of Technology, Piotowo 2, 60-965, Poznań, Poland

    Krzysztof Krawiec

  2. School of Computer Science, The University of Birmingham, B15 2TT, Edgbaston, Birmingham, UK

    Alberto Moraglio

  3. Geisel School of Medicine, Dartmouth College, 03755, Hanover, NH, USA

    Ting Hu

  4. Department of Computer Engineering, Istanbul Technical University, 34469, Masla, Istanbul, Turkey

    A. Şima Etaner-Uyar

  5. Institute of Computer Graphics and Algorithms, Vienna University of Technology, 1040, Vienna, Austria

    Bin Hu

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Otero, F.E.B., Johnson, C.G. (2013). Automated Problem Decomposition for the Boolean Domain with Genetic Programming. In: Krawiec, K., Moraglio, A., Hu, T., Etaner-Uyar, A.Ş., Hu, B. (eds) Genetic Programming. EuroGP 2013. Lecture Notes in Computer Science, vol 7831. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37207-0_15

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  • DOI: https://doi.org/10.1007/978-3-642-37207-0_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37206-3

  • Online ISBN: 978-3-642-37207-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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