Abstract
Program synthesis is a problem domain that due to its importance is tackled by many different fields, one being Genetic Programming. Two variants, Grammar-Guided Genetic Programming (G3P) and PushGP, have been applied to a vast general program synthesis benchmark suite and solved a variety of problems although with varying success rates. While G3P achieved higher success rates on some problems, PushGP was able to find solutions to more problem instances. Reason why G3P fails at some problems might be missing functionality in the grammars or knowledge that has to discovered during the runs. In this paper the current shortcomings of G3P are analysed and the papers contributions include an example of extending grammars for program synthesis, a fairer comparison between PushGP and G3P with a more similar function set as well as new results on problems that have not been solved with G3P and one that has not been solved with PushGP.
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Notes
- 1.
200 for Normal IO, Median and Smallest as proposed in [8].
References
Agapitos, A., Lucas, S.M.: Learning recursive functions with object oriented genetic programming. In: Collet, P., Tomassini, M., Ebner, M., Gustafson, S., Ekárt, A. (eds.) EuroGP 2006. LNCS, vol. 3905, pp. 166–177. Springer, Heidelberg (2006). https://doi.org/10.1007/11729976_15
Forstenlechner, S.: Github repository: HeuristicLab.CFGGP: Provides context free grammar problems for HeuristicLab (2016). https://github.com/t-h-e/HeuristicLab.CFGGP. Accessed 22 Mar 2018
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
Forstenlechner, S., Fagan, D., Nicolau, M., O’Neill, M.: Semantics-based crossover for program synthesis in genetic programming. In: Lutton, E., Legrand, P., Parrend, P., Monmarché, N., Schoenauer, M. (eds.) EA 2017. LNCS, vol. 10764, pp. 58–71. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-78133-4_5
Gulwani, S.: Automating string processing in spreadsheets using input-output examples. In: Proceedings of the 38th Annual ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages, POPL 2011, pp. 317–330. ACM, New York (2011)
Helmuth, T., Spector, L., Matheson, J.: Solving uncompromising problems with lexicase selection. IEEE Trans. Evol. Comput. 19(5), 630–643 (2015)
Helmuth, T., McPhee, N.F., Pantridge, E., Spector, L.: Improving generalization of evolved programs through automatic simplification. In: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2017, pp. 937–944. ACM, Berlin, 15–19 July 2017
Helmuth, T., Spector, L.: General program synthesis benchmark suite. In: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference, GECCO 15, pp. 1039–1046. ACM, Madrid, 11–15 July 2015
Katayama, S.: Recent improvements of MagicHaskeller. In: Schmid, U., Kitzelmann, E., Plasmeijer, R. (eds.) AAIP 2009. LNCS, vol. 5812, pp. 174–193. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-11931-6_9
Keijzer, M., Ryan, C., Murphy, G., Cattolico, M.: Undirected training of run transferable libraries. In: Keijzer, M., Tettamanzi, A., Collet, P., van Hemert, J., Tomassini, M. (eds.) EuroGP 2005. LNCS, vol. 3447, pp. 361–370. Springer, Heidelberg (2005). https://doi.org/10.1007/978-3-540-31989-4_33
Kitzelmann, E.: Inductive programming: a survey of program synthesis techniques. In: Schmid, U., Kitzelmann, E., Plasmeijer, R. (eds.) AAIP 2009. LNCS, vol. 5812, pp. 50–73. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-11931-6_3
McKay, R., Hoai, N., Whigham, P., Shan, Y., O’Neill, M.: Grammar-based genetic programming: a survey. Genet. Program. Evol. Mach. 11(3–4), 365–396 (2010)
O’Neill, M., Nicolau, M., Agapitos, A.: Experiments in program synthesis with grammatical evolution: a focus on integer sorting. In: 2014 IEEE Congress on Evolutionary Computation (CEC), pp. 1504–1511, July 2014
O’Neill, M., Ryan, C.: Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language. Kluwer Academic Publishers, Norwell (2003)
Pantridge, E., Helmuth, T., McPhee, N.F., Spector, L.: On the difficulty of benchmarking inductive program synthesis methods. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO 2017, pp. 1589–1596. ACM, New York (2017)
Saber, T., Fagan, D., Lynch, D., Kucera, S., Claussen, H., O’Neill, M.: Multi-level grammar genetic programming for scheduling in heterogeneous networks. In: Castelli, M., Sekanina, L., Zhang, M., Cagnoni, S., García-Sánchez, P. (eds.) EuroGP 2018. LNCS, vol. 10781, pp. 118–134. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-77553-1_8
Spector, L., Robinson, A.: Genetic programming and autoconstructive evolution with the push programming language. Genet. Program. Evol. Mach. 3(1), 7–40 (2002)
Helmuth, T., Spector, L.: Detailed problem descriptions for general program synthesis benchmark suite. Technical report, School of Computer Science, University of Massachusetts Amherst (2015)
Whigham, P.A.: Grammatical bias for evolutionary learning. Ph.D. thesis, University of New South Wales, Australia (1996)
Yu, T.: A higher-order function approach to evolve recursive programs. In: Yu, T., Riolo, R., Worzel, B. (eds.) Genetic Programming Theory and Practice III. GPEM, pp. 93–108. Springer, Boston (2006). https://doi.org/10.1007/0-387-28111-8_7
Acknowledgments
This research is based upon works supported by the Science Foundation Ireland, under Grant No. 13/IA/1850.
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Forstenlechner, S., Fagan, D., Nicolau, M., O’Neill, M. (2018). Extending Program Synthesis Grammars for Grammar-Guided Genetic Programming. In: Auger, A., Fonseca, C., Lourenço, N., Machado, P., Paquete, L., Whitley, D. (eds) Parallel Problem Solving from Nature – PPSN XV. PPSN 2018. Lecture Notes in Computer Science(), vol 11101. Springer, Cham. https://doi.org/10.1007/978-3-319-99253-2_16
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