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Semantics-Based Crossover for Program Synthesis in Genetic Programming

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Artificial Evolution (EA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10764))

Abstract

Semantic information has been used to create operators that improve performance in genetic programming. As different problem domains have different semantics, extracting semantics and calculating semantic similarity is of tantamount importance to use semantic operators for each domain. To date researchers have struggled to effectively do this beyond the boolean and regression problem domain. In this paper, a semantic similarity-based crossover is tested in the problem domain of program synthesis. For this purpose, a similarity measure based on the execution trace of a program is introduced. Subtree crossover as well as semantic similarity-based crossover are analysed on performance and semantic aspects. The goal is to introduce the Semantic Similarity-based Crossover in the program synthesis domain and to study the effects of using semantic locality. The results show that semantic crossover produces more semantically different children as well as more children that are better than their parents compared to subtree crossover.

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References

  1. Beadle, L., Johnson, C.: Semantically driven mutation in genetic programming. In: 2009 IEEE Congress on Evolutionary Computation, CEC 2009, pp. 1336–1342, May 2009

    Google Scholar 

  2. Beadle, L., Johnson, C.: Semantically driven crossover in genetic programming. In: Wang, J. (ed.) Proceedings of the IEEE World Congress on Computational Intelligence, pp. 111–116. IEEE Computational Intelligence Society, IEEE Press, Hong Kong, 1–6 Jun 2008. http://results.ref.ac.uk/Submissions/Output/1423275

  3. Forstenlechner, S., Fagan, D., Nicolau, M., O’Neill, M.: A grammar design pattern for arbitrary program synthesis problems in genetic programming. In: McDermott, J., Castelli, M., Sekanina, L., Haasdijk, E., García-Sánchez, P. (eds.) EuroGP 2017. LNCS, vol. 10196, pp. 262–277. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-55696-3_17

    Chapter  Google Scholar 

  4. Forstenlechner, S., Nicolau, M., Fagan, D., O’Neill, M.: Introducing semantic-clustering selection in grammatical evolution. In: Johnson, C., Krawiec, K., Moraglio, A., O’Neill, M. (eds.) GECCO 2015 Semantic Methods in Genetic Programming (SMGP 2015) Workshop, pp. 1277–1284. ACM, Madrid, Spain, 11–15 July 2015. https://doi.org/10.1145/2739482.2768502

  5. Galván-López, E., Cody-Kenny, B., Trujillo, L., Kattan, A.: Using semantics in the selection mechanism in genetic programming: a simple method for promoting semantic diversity. In: 2013 IEEE Congress on Evolutionary Computation (CEC), pp. 2972–2979, June 2013

    Google Scholar 

  6. Helmuth, T., Spector, L., Matheson, J.: Solving uncompromising problems with lexicase selection. IEEE Trans. Evol. Comput. 19(5), 630–643 (2015)

    Article  Google Scholar 

  7. Helmuth, T., Spector, L.: General program synthesis benchmark suite. In: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference, GECCO 2015, pp. 1039–1046. ACM, Madrid, Spain, 11–15 July 2015. https://doi.org/10.1145/2739480.2754769

  8. McPhee, N.F., Ohs, B., Hutchison, T.: Semantic building blocks in genetic programming. In: O’Neill, M., Vanneschi, L., Gustafson, S., Esparcia Alcázar, A.I., De Falco, I., Della Cioppa, A., Tarantino, E. (eds.) EuroGP 2008. LNCS, vol. 4971, pp. 134–145. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-78671-9_12

    Chapter  Google Scholar 

  9. Montana, D.J.: Strongly typed genetic programming. Evol. Comput. 3(2), 199–230 (1995). https://doi.org/10.1162/evco.1995.3.2.199

    Article  Google Scholar 

  10. 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. LNCS, vol. 7491, pp. 21–31. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-32937-1_3

    Chapter  Google Scholar 

  11. Nguyen, Q.U., Nguyen, X.H., O’Neill, M.: Semantic aware crossover for genetic programming: the case for real-valued function regression. In: Vanneschi, L., Gustafson, S., Moraglio, A., De Falco, I., Ebner, M. (eds.) EuroGP 2009. LNCS, vol. 5481, pp. 292–302. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-01181-8_25

    Chapter  Google Scholar 

  12. Nguyen, Q.U., Nguyen, X.H., O’Neill, M., McKay, R.I., Galvan-Lopez, E.: Semantically-based crossover in genetic programming: application to real-valued symbolic regression. Genet. Program. Evolvable Mach. 12(2), 91–119 (2011)

    Article  Google Scholar 

  13. Nguyen, Q.U., Nguyen, X.H., O’Neill, M., McKay, R.I., Phong, D.N.: On the roles of semantic locality of crossover in genetic programming. Inf. Sci. 235, 195–213 (2013). http://www.sciencedirect.com/science/article/pii/S0020025513001175

    Article  MathSciNet  MATH  Google Scholar 

  14. Spector, L., Robinson, A.: Genetic programming and autoconstructive evolution with the push programming language. Genet. Program. Evolvable Mach. 3(1), 7–40 (2002). https://doi.org/10.1023/A:1014538503543

    Article  MATH  Google Scholar 

  15. Vanneschi, L., Castelli, M., Silva, S.: A survey of semantic methods in genetic programming. Genet. Program. Evolvable Mach. 15(2), 195–214 (2014). https://doi.org/10.1007/s10710-013-9210-0

    Article  Google Scholar 

  16. Wagner, S., Kronberger, G., Beham, A., Kommenda, M., Scheibenpflug, A., Pitzer, E., Vonolfen, S., Kofler, M., Winkler, S., Dorfer, V., Affenzeller, M.: Architecture and design of the heuristiclab optimization environment. In: Klempous, R., Nikodem, J., Jacak, W., Chaczko, Z. (eds.) Advanced Methods and Applications in Computational Intelligence. Topics in Intelligent Engineering and Informatics, vol. 6, pp. 197–261. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-319-01436-4_10

    Chapter  Google Scholar 

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Acknowledgments

This research is based upon works supported by the Science Foundation Ireland, under Grant No. 13/IA/1850.

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Authors and Affiliations

  1. Natural Computing Research and Applications Group, School of Business, University College Dublin, Dublin, Ireland

    Stefan Forstenlechner, David Fagan, Miguel Nicolau & Michael O’Neill

Authors
  1. Stefan Forstenlechner
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  2. David Fagan
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  3. Miguel Nicolau
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  4. Michael O’Neill
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Corresponding author

Correspondence to Stefan Forstenlechner .

Editor information

Editors and Affiliations

  1. Inria, Thiverval-Grignon, France

    Evelyne Lutton

  2. Inria Bordeaux, University of Bordeaux, Talence, France

    Pierrick Legrand

  3. ECAM Strasbourg-Europe, Schiltigheim, France

    Pierre Parrend

  4. University of Tours, Tours, France

    Nicolas Monmarché

  5. Université Paris-Sud, Orsay, France

    Marc Schoenauer

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Forstenlechner, S., Fagan, D., Nicolau, M., O’Neill, M. (2018). Semantics-Based Crossover for Program Synthesis in Genetic Programming. In: Lutton, E., Legrand, P., Parrend, P., Monmarché, N., Schoenauer, M. (eds) Artificial Evolution. EA 2017. Lecture Notes in Computer Science(), vol 10764. Springer, Cham. https://doi.org/10.1007/978-3-319-78133-4_5

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  • DOI: https://doi.org/10.1007/978-3-319-78133-4_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-78132-7

  • Online ISBN: 978-3-319-78133-4

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