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On the Automatic Design of a Representation for Grammar-Based Genetic Programming

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Genetic Programming (EuroGP 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10781))

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Abstract

A long-standing problem in Evolutionary Computation consists in how to choose an appropriate representation for the solutions. In this work we investigate the feasibility of synthesizing a representation automatically, for the large class of problems whose solution spaces can be defined by a context-free grammar. We propose a framework based on a form of meta-evolution in which individuals are candidate representations expressed with an ad hoc language that we have developed to this purpose. Individuals compete and evolve according to an evolutionary search aimed at optimizing such representation properties as redundancy, locality, uniformity of redundancy.

We assessed experimentally three variants of our framework on established benchmark problems and compared the resulting representations to human-designed representations commonly used (e.g., classical Grammatical Evolution). The results are promising in the sense that the evolved representations indeed exhibit better properties than the human-designed ones. Furthermore, while those improved properties do not result in a systematic improvement of search effectiveness, some of the evolved representations do improve search effectiveness over the human-designed baseline.

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Notes

  1. 1.

    https://github.com/ericmedvet/evolved-ge.

References

  1. Altenberg, L.: Probing the axioms of evolutionary algorithm design: commentary on “On the mapping of genotype to phenotype in evolutionary algorithms” by Peter A. Whigham, Grant Dick, and James Maclaurin. Genet. Program. Evolvable Mach. 18(3), 363–367 (2017)

    Article  Google Scholar 

  2. Castelli, M., Manzoni, L., Vanneschi, L., Silva, S., Popovič, A.: Self-tuning geometric semantic genetic programming. Genet. Program. Evolvable Mach. 17(1), 55–74 (2016)

    Article  Google Scholar 

  3. Correia, M.B.: A study of redundancy and neutrality in evolutionary optimization. Evol. Comput. 21(3), 413–443 (2013)

    Article  Google Scholar 

  4. Cruz-Salinas, A.F., Perdomo, J.G.: Self-adaptation of genetic operators through genetic programming techniques. In: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2017, pp. 913–920. ACM, New York (2017)

    Google Scholar 

  5. De Jong, K.: Parameter setting in EAs: a 30 year perspective. In: Lobo, F.G., Lima, C.F., Michalewicz, Z. (eds.) Parameter Setting in Evolutionary Algorithms, pp. 1–18. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-69432-8_1

    Google Scholar 

  6. Fogel, D.B., Fogel, L.J., Atmar, J.W.: Meta-evolutionary programming. In: 1991 Conference Record of the Twenty-Fifth Asilomar Conference on Signals, Systems and Computers, pp. 540–545. IEEE (1991)

    Google Scholar 

  7. Foster, J.A.: Taking “biology” just seriously enough: commentary on “On the mapping of genotype to phenotype in evolutionary algorithms” by Peter A. Whigham, Grant Dick, and James Maclaurin. Genet. Program. Evolvable Mach. 18(3), 395–398 (2017)

    Article  Google Scholar 

  8. Hong, L., Drake, J.H., Woodward, J.R., Özcan, E.: A hyper-heuristic approach to automated generation of mutation operators for evolutionary programming. Appl. Soft Comput. 62, 162–175 (2018)

    Article  Google Scholar 

  9. Lourenço, N., Pereira, F.B., Costa, E.: SGE: a structured representation for grammatical evolution. In: Bonnevay, S., Legrand, P., Monmarché, N., Lutton, E., Schoenauer, M. (eds.) EA 2015. LNCS, vol. 9554, pp. 136–148. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-31471-6_11

    Google Scholar 

  10. Mckay, R.I., Hoai, N.X., Whigham, P.A., Shan, Y., O’Neill, M.: Grammar-based genetic programming: a survey. Genet. Program. Evolvable Mach. 11(3–4), 365–396 (2010)

    Article  Google Scholar 

  11. Medvet, E.: A comparative analysis of dynamic locality and redundancy in grammatical evolution. In: McDermott, J., Castelli, M., Sekanina, L., Haasdijk, E., García-Sánchez, P. (eds.) EuroGP 2017. LNCS, vol. 10196, pp. 326–342. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-55696-3_21

    Chapter  Google Scholar 

  12. Medvet, E.: Hierarchical grammatical evolution. In: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO (2017)

    Google Scholar 

  13. Medvet, E., Bartoli, A., Squillero, G.: An effective diversity promotion mechanism in grammatical evolution. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO 2017, pp. 247–248. ACM, New York (2017)

    Google Scholar 

  14. Medvet, E., Daolio, F., Tagliapietra, D.: Evolvability in grammatical evolution. In: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2017, pp. 977–984. ACM, New York (2017)

    Google Scholar 

  15. Miller, J.F., Smith, S.L.: Redundancy and computational efficiency in Cartesian genetic programming. IEEE Trans. Evol. Comput. 10(2), 167–174 (2006)

    Article  Google Scholar 

  16. O’Neill, M., Brabazon, A., Nicolau, M., Garraghy, S.M., Keenan, P.: \(\pi \)Grammatical evolution. In: Deb, K. (ed.) GECCO 2004. LNCS, vol. 3103, pp. 617–629. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24855-2_70

    Chapter  Google Scholar 

  17. O’Neill, M., Ryan, C.: Genetic code degeneracy: implications for grammatical evolution and beyond. In: Floreano, D., Nicoud, J.-D., Mondada, F. (eds.) ECAL 1999. LNCS (LNAI), vol. 1674, pp. 149–153. Springer, Heidelberg (1999). https://doi.org/10.1007/3-540-48304-7_21

    Chapter  Google Scholar 

  18. O’Neill, M., Ryan, C.: Grammatical evolution by grammatical evolution: the evolution of grammar and genetic code. In: Keijzer, M., O’Reilly, U.-M., Lucas, S., Costa, E., Soule, T. (eds.) EuroGP 2004. LNCS, vol. 3003, pp. 138–149. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24650-3_13

    Chapter  Google Scholar 

  19. Pagie, L., Hogeweg, P.: Evolutionary consequences of coevolving targets. Evol. Comput. 5(4), 401–418 (1997)

    Article  Google Scholar 

  20. Pappa, G.L., Ochoa, G., Hyde, M.R., Freitas, A.A., Woodward, J., Swan, J.: Contrasting meta-learning and hyper-heuristic research: the role of evolutionary algorithms. Genet. Program. Evolvable Mach. 15(1), 3–35 (2014)

    Article  Google Scholar 

  21. Qin, A.K., Huang, V.L., Suganthan, P.N.: Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans. Evol. Comput. 13(2), 398–417 (2009)

    Article  Google Scholar 

  22. Rothlauf, F.: Representations for genetic and evolutionary algorithms. In: Rothlauf, F. (ed.) Representations for Genetic and Evolutionary Algorithms, pp. 9–32. Springer, Heidelberg (2006). https://doi.org/10.1007/3-540-32444-5_2

    Chapter  Google Scholar 

  23. Rothlauf, F., Goldberg, D.E.: Redundant representations in evolutionary computation. Evol. Comput. 11(4), 381–415 (2003)

    Article  Google Scholar 

  24. Ryan, C.: A rebuttal to Whigham, Dick, and Maclaurin by one of the inventors of grammatical evolution: commentary on “On the mapping of genotype to phenotype in evolutionary algorithms” by Peter A. Whigham, Grant Dick, and James Maclaurin. Genet. Program. Evolvable Mach. 18, 385–389 (2017)

    Article  Google Scholar 

  25. Ryan, C., Collins, J.J., Neill, M.O.: Grammatical evolution: evolving programs for an arbitrary language. In: Banzhaf, W., Poli, R., Schoenauer, M., Fogarty, T.C. (eds.) EuroGP 1998. LNCS, vol. 1391, pp. 83–96. Springer, Heidelberg (1998). https://doi.org/10.1007/BFb0055930

    Chapter  Google Scholar 

  26. Saravanan, N., Fogel, D.B., Nelson, K.M.: A comparison of methods for self-adaptation in evolutionary algorithms. BioSystems 36(2), 157–166 (1995)

    Article  Google Scholar 

  27. Scott, E.O., Bassett, J.K.: Learning genetic representations for classes of real-valued optimization problems. In: Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation, pp. 1075–1082. ACM (2015)

    Google Scholar 

  28. Simões, L.F., Izzo, D., Haasdijk, E., Eiben, A.E.: Self-adaptive genotype-phenotype maps: neural networks as a meta-representation. In: Bartz-Beielstein, T., Branke, J., Filipič, B., Smith, J. (eds.) PPSN 2014. LNCS, vol. 8672, pp. 110–119. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10762-2_11

    Google Scholar 

  29. Spector, L.: Introduction to the peer commentary special section on “On the mapping of genotype to phenotype in evolutionary algorithms” by Peter A. Whigham, Grant Dick, and James Maclaurin. Genet. Program. Evolvable Mach. 18(3), 351–352 (2017)

    Article  Google Scholar 

  30. Squillero, G., Tonda, A.: (over-)realism in evolutionary computation: commentary on “On the mapping of genotype to phenotype in evolutionary algorithms” by Peter A. Whigham, Grant Dick, and James Maclaurin. Genet. Program. Evolvable Mach. 18, 391–393 (2017)

    Article  Google Scholar 

  31. Thorhauer, A.: On the non-uniform redundancy in grammatical evolution. In: Handl, J., Hart, E., Lewis, P.R., López-Ibáñez, M., Ochoa, G., Paechter, B. (eds.) PPSN 2016. LNCS, vol. 9921, pp. 292–302. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-45823-6_27

    Chapter  Google Scholar 

  32. Thorhauer, A., Rothlauf, F.: On the locality of standard search operators in grammatical evolution. In: Bartz-Beielstein, T., Branke, J., Filipič, B., Smith, J. (eds.) PPSN 2014. LNCS, vol. 8672, pp. 465–475. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10762-2_46

    Google Scholar 

  33. Vanneschi, L., Castelli, M., Manzoni, L.: The K landscapes: a tunably difficult benchmark for genetic programming. In: Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, pp. 1467–1474. ACM (2011)

    Google Scholar 

  34. Whigham, P.A., Dick, G., Maclaurin, J.: On the mapping of genotype to phenotype in evolutionary algorithms. Genet. Program. Evolvable Mach. 18, 353–361 (2017)

    Article  Google Scholar 

  35. Whigham, P.A., Dick, G., Maclaurin, J., Owen, C.A.: Examining the best of both worlds of grammatical evolution. In: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, pp. 1111–1118. ACM (2015)

    Google Scholar 

  36. Whigham, P.A., et al.: Grammatically-based genetic programming. In: Proceedings of the Workshop on Genetic Programming: From Theory to Real-World Applications, vol. 16, pp. 33–41 (1995)

    Google Scholar 

  37. White, D.R., Mcdermott, J., Castelli, M., Manzoni, L., Goldman, B.W., Kronberger, G., Jaśkowski, W., O’Reilly, U.M., Luke, S.: Better GP benchmarks: community survey results and proposals. Genet. Program. Evolvable Mach. 14(1), 3–29 (2013)

    Article  Google Scholar 

  38. Wong, P.-K., Wong, M.-L., Leung, K.-S.: Hierarchical knowledge in self-improving grammar-based genetic programming. In: Handl, J., Hart, E., Lewis, P.R., López-Ibáñez, M., Ochoa, G., Paechter, B. (eds.) PPSN 2016. LNCS, vol. 9921, pp. 270–280. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-45823-6_25

    Chapter  Google Scholar 

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Authors and Affiliations

  1. Department of Engineering and Architecture, University of Trieste, Trieste, Italy

    Eric Medvet & Alberto Bartoli

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  1. Eric Medvet
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  2. Alberto Bartoli
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Correspondence to Eric Medvet .

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Editors and Affiliations

  1. Universidade Nova de Lisboa, Lisbon, Portugal

    Mauro Castelli

  2. Brno University of Technology, Brno, Czech Republic

    Lukas Sekanina

  3. Victoria University of Wellington, Wellington, New Zealand

    Mengjie Zhang

  4. University of Parma, Parma, Italy

    Stefano Cagnoni

  5. University of Cádiz, Cádiz, Spain

    Pablo García-Sánchez

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Medvet, E., Bartoli, A. (2018). On the Automatic Design of a Representation for Grammar-Based Genetic Programming. In: Castelli, M., Sekanina, L., Zhang, M., Cagnoni, S., García-Sánchez, P. (eds) Genetic Programming. EuroGP 2018. Lecture Notes in Computer Science(), vol 10781. Springer, Cham. https://doi.org/10.1007/978-3-319-77553-1_7

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  • DOI: https://doi.org/10.1007/978-3-319-77553-1_7

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