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Program Optimisation with Dependency Injection

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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

For many real-world problems, there exist non-deterministic heuristics which generate valid but possibly sub-optimal solutions. The program optimisation with dependency injection method, introduced here, allows such a heuristic to be placed under evolutionary control, allowing search for the optimum. Essentially, the heuristic is “fooled” into using a genome, supplied by a genetic algorithm, in place of the output of its random number generator. The method is demonstrated with generative heuristics in the domains of 3D design and communications network design. It is also used in novel approaches to genetic programming.

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References

  1. Brameier, M., Banzhaf, W.: Linear genetic programming. Springer (2006)

    Google Scholar 

  2. Byrne, J., Fenton, M., Hemberg, E., McDermott, J., O’Neill, M., Shotton, E., Nally, C.: Combining Structural Analysis and Multi-Objective Criteria for Evolutionary Architectural Design. In: Di Chio, C., Brabazon, A., Di Caro, G.A., Drechsler, R., Farooq, M., Grahl, J., Greenfield, G., Prins, C., Romero, J., Squillero, G., Tarantino, E., Tettamanzi, A.G.B., Urquhart, N., Uyar, A.Ş. (eds.) EvoApplications 2011, Part II. LNCS, vol. 6625, pp. 204–213. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  3. Carroll, P., McGarraghy, S.: A decomposition algorithm for the ring spur assignment problem. International Transactions in Operational Research (2012)

    Google Scholar 

  4. Hemberg, E., Veeramachaneni, K., McDermott, J., Berzan, C., O’Reilly, U.M.: An investigation of local patterns for estimation of distribution genetic programming. In: Proc. GECCO. ACM (2012)

    Google Scholar 

  5. Hoos, H.H.: Programming by optimisation. Tech. Rep. TR-2010, Department of Computer Science, University of British Columbia (2010)

    Google Scholar 

  6. Hornby, G.S., Pollack, J.B.: The advantages of generative grammatical encodings for physical design. In: Proc. CEC, pp. 600–607. IEEE (2001)

    Google Scholar 

  7. Keijzer, M., O’Neill, M., Ryan, C., Cattolico, M.: Grammatical Evolution Rules: The Mod and the Bucket Rule. In: Foster, J.A., Lutton, E., Miller, J., Ryan, C., Tettamanzi, A.G.B. (eds.) EuroGP 2002. LNCS, vol. 2278, pp. 123–130. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  8. Kilmartin, P., Flynn, M.: Quantum Annealing in Management Science & Analytics: An investigation of applying QA Techniques to the Ring Spur Assignment Problem. Master’s thesis, University College Dublin Business School (2012)

    Google Scholar 

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

    MATH  Google Scholar 

  10. Krawiec, K., Lichocki, P.: Approximating geometric crossover in semantic space. In: Proc. GECCO, pp. 987–994. ACM, New York (2009)

    Chapter  Google Scholar 

  11. McDermott, J., Byrne, J., Swafford, J.M., Hemberg, M., McNally, C., Shotton, E., Hemberg, E., Fenton, M., O’Neill, M.: String-rewriting grammars for evolutionary architectural design. Environment and Planning B: Planning and Design 39(4), 713–731 (2012), http://www.envplan.com/abstract.cgi?id=b38037

    Article  Google Scholar 

  12. Miller, J.F., Thomson, P.: Cartesian Genetic Programming. In: Poli, R., Banzhaf, W., Langdon, W.B., Miller, J., Nordin, P., Fogarty, T.C. (eds.) EuroGP 2000. LNCS, vol. 1802, pp. 121–132. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  13. 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 

  14. O’Neill, M., Ryan, C.: Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language. Kluwer Academic Publishers (2003)

    Google Scholar 

  15. Pagie, L., Hogeweg, P.: Evolutionary Consequences of Coevolving Targets. Evolutionary Computation 5, 401–418 (1997)

    Article  Google Scholar 

  16. Rothlauf, F.: Representations for Genetic and Evolutionary Algorithms, 2nd edn. Physica-Verlag (2006)

    Google Scholar 

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McDermott, J., Carroll, P. (2013). Program Optimisation with Dependency Injection. 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_12

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  • DOI: https://doi.org/10.1007/978-3-642-37207-0_12

  • 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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