Skip to main content
Log in

Response to comments on “Jaws 30”

  • Reply
  • Published:
Genetic Programming and Evolvable Machines Aims and scope Submit manuscript

The Original Article was published on 22 November 2023

The Original Article was published on 22 November 2023

The Original Article was published on 22 November 2023

The Original Article was published on 22 November 2023

The Original Article was published on 22 November 2023

The Original Article was published on 22 November 2023

The Original Article was published on 22 November 2023

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2

Notes

  1. Cf. Veni, vidi, vici (I came; I saw; I conquered) Julius Caesar, 47 BC.

  2. At EuroGP 2000 I did not follow today’s advice and the information about glucose went into the figure’s caption rather than in the figure itself.

  3. At his GECCO 2023 invited keynote Kenneth De Jong said we should be careful to avoid designing our evolutionary computation algorithms with more synchronisation points than necessary.

  4. Since it is known that disruptions caused by mutation or crossover often fail to propagate up deeply nesting programs to impact fitness [47,48,49], another argument for splitting up programs is to ensure each member of the team or ensemble is exposed to the fitness testing environment, whilst each remains relatively shallow.

  5. A miscellaneous collection of GP tools can be found at http://www.cs.ucl.ac.uk/staff/W.Langdon/homepages.html#6.

  6. Abstract syntax trees are often used by high level language compilers during syntax analysis. ASTs written in XML typically contain many diverse types and strongly typed genetic programming, STGP [87], crossover and mutation are readily implemented with XML [65].

References

  1. J.R. Koza, Genetic programming: on the programming of computers by means of natural selection. MIT Press, Cambridge, MA, USA (1992), http://mitpress.mit.edu/books/genetic-programming

  2. W.B. Langdon, Jaws 30. Genetic programming and evolvable machines peer commentary on the thirtieth anniversary of genetic programming: on the programming of computers by means of natural selection

  3. G. Squillero, A. Tonda, Veni, vidi, evolvi. Genetic Programming and Evolvable Machines Peer Commentary on the Thirtieth Anniversary of Genetic Programming: On the Programming of Computers by Means of Natural Selection

  4. W.B. Langdon et al., Comparison of AdaBoost and genetic programming for combining neural networks for drug discovery. In: Raidl, G.R., et al. (eds.) Applications of Evolutionary Computing, EvoWorkshops2003: EvoBIO, EvoCOP, EvoIASP, EvoMUSART, EvoROB, EvoSTIM. LNCS, vol. 2611, pp. 87–98. Springer-Verlag, University of Essex, UK (14-16 Apr 2003), https://doi.org/10.1007/3-540-36605-9_9

  5. F. Assuncao et al., DENSER: deep evolutionary network structured representation. Genet. Program. Evol. Mach. 20(1), 5–35 (2019). https://doi.org/10.1007/s10710-018-9339-y

    Article  Google Scholar 

  6. M. Harman, B.F. Jones, Search based software engineering. Inf. Softw. Technol. 43(14), 833–839 (2001). https://doi.org/10.1016/S0950-5849(01)00189-6

    Article  Google Scholar 

  7. S. Forrest et al., A genetic programming approach to automated software repair. In: Raidl, G., et al. (eds.) GECCO ’09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation. pp. 947–954. ACM, Montreal (8-12 Jul 2009), https://doi.org/10.1145/1569901.1570031, gECCO 2019 10-Year Most Influential Paper Award, Best paper

  8. C. Le Goues et al., Automated program repair. Commun. ACM 62(12), 56–65 (2019). https://doi.org/10.1145/3318162

    Article  Google Scholar 

  9. W.B. Langdon, Genetic improvement of programs. In: Matousek, R. (ed.) 18th International Conference on Soft Computing, MENDEL 2012. Brno University of Technology, Brno, Czech Republic (27-29 Jun 2012), http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Langdon_2012_mendel.pdf, invited keynote

  10. W.B. Langdon et al., Genetic improvement of GPU software. Genet. Program. Evol. Mach. 18(1), 5–44 (2017). https://doi.org/10.1007/s10710-016-9273-9

    Article  Google Scholar 

  11. W.B. Langdon, R. Lorenz, Improving SSE parallel code with grow and graft genetic programming. In: Petke, J., et al. (eds.) GI-2017. pp. 1537–1538. ACM, Berlin (15-19 Jul 2017), https://doi.org/10.1145/3067695.3082524

  12. S.O. Haraldsson et al., Fixing bugs in your sleep: How genetic improvement became an overnight success, in Petke, J., et al. (eds.) GI-2017. pp. 1513–1520. ACM, Berlin (15-19 Jul 2017), https://doi.org/10.1145/3067695.3082517, best paper

  13. N. Alshahwan, Industrial experience of genetic improvement in Facebook, in Petke, J., et al. (eds.) GI-2019, ICSE workshops proceedings. p. 1. IEEE, Montreal (28 May 2019), https://doi.org/10.1109/GI.2019.00010, invited Keynote

  14. S. Kirbas et al., On the introduction of automatic program repair in Bloomberg. IEEE Softw. 38(4), 43–51 (2021). https://doi.org/10.1109/MS.2021.3071086

    Article  Google Scholar 

  15. G. Squillero, Artificial evolution in computer aided design: from the optimization of parameters to the creation of assembly programs. Computing 93(2–4), 103–120 (2011). https://doi.org/10.1007/s00607-011-0157-9

    Article  MathSciNet  Google Scholar 

  16. M. Castelli, Commentary for the GPEM peer commentary special section on W. B. Langdon’s “Jaws 30”. Genetic Programming and Evolvable Machines Peer Commentary on the Thirtieth Anniversary of Genetic Programming: On the Programming of Computers by Means of Natural Selection

  17. W.B. Langdon, R. Poli, Foundations of genetic programming. Springer-Verlag (2002). https://doi.org/10.1007/978-3-662-04726-2

  18. M. Hort et al., Multi-objective search for gender-fair and semantically correct word embeddings. Appl. Soft Comput. 133, 109916 (2023). https://doi.org/10.1016/j.asoc.2022.109916

    Article  Google Scholar 

  19. E. Daka et al., Modeling readability to improve unit tests. In: Nitto, E.D., et al. (eds.) Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering, ESEC/FSE 2015, Bergamo, Italy, August 30 - September 4, 2015. pp. 107–118. ACM (2015), https://doi.org/10.1145/2786805.2786838

  20. A. Panichella et al., Revisiting test smells in automatically generated tests: Limitations, pitfalls, and opportunities. In: 2020 IEEE International Conference on Software Maintenance and Evolution (ICSME). pp. 523–533. Adelaide (2020), https://doi.org/10.1109/ICSME46990.2020.00056

  21. W.B. Langdon, J.P. Nordin, Seeding GP populations, in Poli, R., et al. (eds.) Genetic Programming, Proceedings of EuroGP’2000. LNCS, vol. 1802, pp. 304–315. Springer-Verlag, Edinburgh (15-16 Apr 2000), https://doi.org/10.1007/978-3-540-46239-2_23

  22. M.I. Heywood, W. B. Langdon “JAWS 30”. Genetic Programming and Evolvable Machines Peer Commentary on the Thirtieth Anniversary of Genetic Programming: On the Programming of Computers by Means of Natural Selection

  23. G.E. Moore, Cramming more components onto integrated circuits. Electronics 38(8), 114–117 (1965)

    Google Scholar 

  24. W.B. Langdon et al., Genetically improved software with fewer data caches misses. In: Proceedings of the 2023 Genetic and Evolutionary Computation Conference. GECCO ’23, Association for Computing Machinery, Lisbon, Portugal (15-19 Jul 2023), https://doi.org/10.1145/3583133.3590542, forthcoming

  25. D. Andre, J.R. Koza, Parallel genetic programming on a network of transputers. In: Rosca, J.P. (ed.) Proceedings of the Workshop on Genetic Programming: From Theory to Real-World Applications. pp. 111–120. Tahoe City, California, USA (9 Jul 1995), http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/andre_1995_parallel.pdf

  26. D. Andre, J.R. Koza, A parallel implementation of genetic programming that achieves super-linear performance. Inf. Sci. 106(3–4), 201–218 (1998). https://doi.org/10.1016/S0020-0255(97)10011-1

    Article  Google Scholar 

  27. A. Fukunaga, et al., A genome compiler for high performance genetic programming. In: Koza, J.R., et al. (eds.) Genetic Programming 1998: Proceedings of the Third Annual Conference. pp. 86–94. Morgan Kaufmann, University of Wisconsin, Madison, Wisconsin, USA (22-25 Jul 1998), http://metahack.org/gp98-compiler.pdf

  28. H. Juille, J.B. Pollack, Massively parallel genetic programming. In: Angeline, P.J., Kinnear, Jr., K.E. (eds.) Advances in Genetic Programming 2, chap. 17, pp. 339–357. MIT Press, Cambridge, MA, USA (1996), https://doi.org/10.7551/mitpress/1109.003.0023

  29. W.B. Langdon, W. Banzhaf, A SIMD interpreter for genetic programming on GPU graphics cards. In: O’Neill, M., et al. (eds.) Proceedings of the 11th European Conference on Genetic Programming, EuroGP 2008. Lecture Notes in Computer Science, vol. 4971, pp. 73–85. Springer, Naples (26-28 Mar 2008), https://doi.org/10.1007/978-3-540-78671-9_7

  30. O. Maitre, Genetic programming on GPGPU cards using EASEA. In: Tsutsui, S., Collet, P. (eds.) Massively Parallel Evolutionary Computation on GPGPUs, chap. 11, pp. 227–248. Natural Computing Series, Springer (2013), https://doi.org/10.1007/978-3-642-37959-8_11

  31. C. Ortega-Sanchez et al., Embryonics: a bio-inspired cellular architecture with fault-tolerant properties. Genet. Program. Evol. Mach. 1(3), 187–215 (2000). https://doi.org/10.1023/A:1010080629099

    Article  MATH  Google Scholar 

  32. C. Pedraza et al., Genetic algorithm for Boolean minimization in an FPGA cluster. The Journal of Supercomputing 58(2), 244–252 (2011), https://doi.org/10.1007/s11227-010-0401-7, special issue on HPC in computational Science and Engineering. Part I

  33. L. Spector, Automatic Quantum Computer Programming: A Genetic Programming Approach, Genetic Programming, vol. 7. Kluwer Academic Publishers, Boston/Dordrecht/New York/London (Jun 2004), https://doi.org/10.1007/978-0-387-36791-0

  34. G. O’Brien, J. Clark, Using genetic improvement to retarget quantum software on differing hardware. In: Petke, J., et al. (eds.) GI @ ICSE 2021. pp. 31–38. IEEE, internet (30 May 2021), https://doi.org/10.1109/GI52543.2021.00015, winner Best Presentation

  35. W.B. Langdon, Large scale bioinformatics data mining with parallel genetic programming on graphics processing units. In: Fernandez de Vega, F., Cantu-Paz, E. (eds.) Parallel and Distributed Computational Intelligence, Studies in Computational Intelligence, vol. 269, chap. 5, pp. 113–141. Springer (Jan 2010), https://doi.org/10.1007/978-3-642-10675-0_6

  36. M. Ridley, The Red Queen, Sex and the Evolution of Human Nature. Penquin (1993), https://en.wikipedia.org/wiki/The_Red_Queen:_Sex_and_the_Evolution_of_Human_Nature

  37. W.B. Langdon, Genetic programming and data structures: genetic programming + data structures = automatic programming! Genet. Program. (1998). https://doi.org/10.1007/978-1-4615-5731-9

    Article  MATH  Google Scholar 

  38. J.R. Koza, Genetic Programming II: Automatic Discovery of Reusable Programs. MIT Press, Cambridge Massachusetts (May 1994), http://www.genetic-programming.org/gpbook2toc.html

  39. M. Ebner, Coevolution and the red queen effect shape virtual plants. Genet. Program. Evol. Mach. 7(1), 103–123 (2006). https://doi.org/10.1007/s10710-006-7013-2

    Article  Google Scholar 

  40. A. Arcuri, X. Yao, Coevolving programs and unit tests from their specification, in IEEE International Conference on Automated Software Engineering (ASE). Atlanta, Georgia, USA (Nov 5-9 2007), https://doi.org/10.1145/1321631.1321693

  41. W.B. Langdon, B.F. Buxton, Genetic programming for combining classifiers, in Spector, L., et al. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001). pp. 66–73. Morgan Kaufmann, San Francisco, California, USA (7-11 Jul 2001), http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/WBL_gecco2001_roc.pdf

  42. M. Virgolin, Genetic programming is naturally suited to evolve bagging ensembles. In: Chicano, F., et al. (eds.) Proceedings of the 2021 Genetic and Evolutionary Computation Conference. pp. 830–839. GECCO ’21, Association for Computing Machinery, internet (Jul 10-14 2021), https://doi.org/10.1145/3449639.3459278

  43. M. Brameier, W. Banzhaf, Evolving teams of predictors with linear genetic programming. Genet. Program. Evol. Mach. 2(4), 381–407 (2001). https://doi.org/10.1023/A:1012978805372

    Article  MATH  Google Scholar 

  44. J. Louchet, Using an individual evolution strategy for stereovision. Genet. Program. Evol. Mach. 2(2), 101–109 (2001). https://doi.org/10.1023/A:1011544128842

    Article  MATH  Google Scholar 

  45. F.H. Bennett III, Emergence of a multi-agent architecture and new tactics for the ant colony foraging problem using genetic programming, in: Maes, P., et al. (eds.) Proceedings of the Fourth International Conference on Simulation of Adaptive Behavior: From animals to animats 4. pp. 430–439. MIT Press, Cape Code, USA (9-13 Sep 1996), https://doi.org/10.7551/mitpress/3118.003.0044

  46. M. Georgiev et al., Performance analysis and comparison on heterogeneous and homogeneous multi-agent societies in correlation to their average capabilities, in 2018 57th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE). pp. 674–679. Nara, Japan (11-14 Sep 2018), https://doi.org/10.23919/SICE.2018.8492713

  47. J. Petke et al., Software robustness: A survey, a theory, and some prospects, in Avgeriou, P., Zhang, D. (eds.) ESEC/FSE 2021, Ideas, Visions and Reflections. pp. 1475–1478. ACM, Athens, Greece (23-28 Aug 2021), https://doi.org/10.1145/3468264.3473133

  48. W.B. Langdon, Genetic programming convergence. Genet. Program. Evol. Mach. 23(1), 71–104 (2022). https://doi.org/10.1007/s10710-021-09405-9

    Article  Google Scholar 

  49. W.B. Langdon, A trillion genetic programming instructions per second. ArXiv (6 May 2022), https://arxiv.org/abs/2205.03251

  50. A. Bartoli et al., Commentary on “Jaws 30”, by W. B. Langdon. Genetic Programming and Evolvable Machines Peer Commentary on the Thirtieth Anniversary of Genetic Programming: On the Programming of Computers by Means of Natural Selection

  51. M. Schmidt, H. Lipson, Distilling free-form natural laws from experimental data. Science 324(5923), 81–85 (2009). https://doi.org/10.1126/science.1165893

    Article  Google Scholar 

  52. N. Savage, Automating scientific discovery. Commun. ACM 55(5), 9–11 (2012). https://doi.org/10.1145/2160718.2160723

    Article  Google Scholar 

  53. R. Dubcakova, Eureqa: software review. Genet. Program. Evol. Mach. 12(2), 173–178 (2011). https://doi.org/10.1007/s10710-010-9124-z

    Article  Google Scholar 

  54. A. Elyasaf, M. Sipper, Software review: the heuristiclab framework. Genet. Program. Evol. Mach. 15(2), 215–218 (2014). https://doi.org/10.1007/s10710-014-9214-4

    Article  Google Scholar 

  55. J. Kim, S. Yoo, Software review: DEAP (distributed evolutionary algorithm in python) library. Genet. Program. Evol. Mach. 20(1), 139–142 (2019). https://doi.org/10.1007/s10710-018-9341-4

    Article  Google Scholar 

  56. U. Abdulkarimova et al., The PARSEC machine: a non-Newtonian supra-linear super-computer. Azerbaijan J. High Perf. Comput. 2(2), 122–140 (2019). https://doi.org/10.32010/26166127.2019.2.2.122.140

    Article  Google Scholar 

  57. D.R. White, Software review: the ECJ toolkit. Genet. Program. Evol. Mach. 13(1), 65–67 (2012). https://doi.org/10.1007/s10710-011-9148-z

    Article  MathSciNet  Google Scholar 

  58. G. Espada et al., Data types as a more ergonomic frontend for grammar-guided genetic programming, in Scholz, B., Kameyama, Y. (eds.) 21st ACM SIGPLAN International Conference on Generative Programming: Concepts and Experiences (GPCE 2022). pp. 86–94. ACM, Auckland, New Zealand (Dec 6-7 2022), https://doi.org/10.1145/3564719.3568697

  59. C. Le Goues et al., GenProg: a generic method for automatic software repair. IEEE Trans. Softw. Eng. 38(1), 54–72 (2012). https://doi.org/10.1109/TSE.2011.104

    Article  Google Scholar 

  60. D.R. White, GI in no time, in Petke, J., et al. (eds.) GI-2017. pp. 1549–1550. ACM, Berlin (15-19 Jul 2017), https://doi.org/10.1145/3067695.3082515

  61. M. Watkinson, A. Brownlee, Updating Gin’s profiler for current java, in Nowack, V., et al. (eds.) 12th International Workshop on Genetic Improvement @ICSE 2023. pp. 23–28. IEEE, Melbourne, Australia (20 May 2023), https://doi.org/10.1109/GI59320.2023.00015

  62. I. Atmosukarto, GPLAB: software review. Genet. Program. Evol. Mach. 12(4), 457–459 (2012). https://doi.org/10.1007/s10710-011-9142-5

    Article  Google Scholar 

  63. A.H. Gandomi, E. Atefi, Software review: the GPTIPS platform. Genet. Program. Evol. Mach. 21(1–2), 273–280 (2020). https://doi.org/10.1007/s10710-019-09366-0

    Article  Google Scholar 

  64. A. Tonda, Inspyred: bio-inspired algorithms in python. Genet. Program. Evol. Mach. 21(1–2), 269–272 (2020). https://doi.org/10.1007/s10710-019-09367-z

    Article  Google Scholar 

  65. A. Blot, J. Petke, MAGPIE: Machine automated general performance improvement via evolution of software. arXiv (4 Aug 2022), https://doi.org/10.48550/arxiv.2208.02811

  66. T.M. Vu, Software review: Pony GE2. Genet. Program. Evol. Mach. 22(3), 383–385 (2021). https://doi.org/10.1007/s10710-021-09409-5

    Article  Google Scholar 

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

    Article  MATH  Google Scholar 

  68. F. Baeta et al., TensorGP - genetic programming engine in TensorFlow, in Castillo, P., Jimenez-Laredo, J. (eds.) 24th International Conference, EvoApplications 2021. LNCS, vol. 12694, pp. 763–778. Springer Verlag, virtual event (7-9 Apr 2021), https://doi.org/10.1007/978-3-030-72699-7_48

  69. A. Danandeh Mehr et al., Genetic programming in water resources engineering: a state-of-the-art review. J. Hydrol. 566, 643–667 (2018). https://doi.org/10.1016/j.jhydrol.2018.09.043

    Article  Google Scholar 

  70. Q. Zhang et al., Genetic programming in civil engineering: advent, applications and future trends. Artif. Intell. Rev. 54, 1863–1885 (2021). https://doi.org/10.1007/s10462-020-09894-7

    Article  Google Scholar 

  71. R.S. Olson et al., Automating biomedical data science through tree-based pipeline optimization. In: Squillero, G., Burelli, P. (eds.) Proceedings of the 19th European Conference on Applications of Evolutionary Computation, EvoApplications 2016, Part I. LNCS, vol. 9597, pp. 123–137. Springer, Porto, Portugal (Mar 30 - Apr 1 2016), https://doi.org/10.1007/978-3-319-31204-0_9, best paper, EvoBio track

  72. R.S. Olson et al., A system for accessible artificial intelligence. In: Banzhaf, W., et al. (eds.) Genetic Programming Theory and Practice XV. pp. 121–134. Genetic and Evolutionary Computation, Springer, University of Michigan in Ann Arbor, USA (May 18–20 2017), https://doi.org/10.1007/978-3-319-90512-9_8

  73. R.J. Andrews et al., A map of the SARS-CoV-2 RNA structurome. NAR Genom. Bioinf. 3(2), lqab043 (2021). https://doi.org/10.1093/nargab/lqab043

    Article  Google Scholar 

  74. W. Banzhaf et al., (eds.): Genetic Programming Theory and Practice XVIII. Genetic and Evolutionary Computation, Springer, East Lansing, USA (19-21 May 2021), https://doi.org/10.1007/978-981-16-8113-4

  75. W. La Cava et al., Contemporary symbolic regression methods and their relative performance. In: Vanschoren, J., Yeung, S. (eds.) Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks. vol. 1. Curran (2021), https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/hash/c0c7c76d30bd3dcaefc96f40275bdc0a-Abstract-round1.html

  76. J. Petke et al., (eds.): 10th Genetic Improvement Workshop (GI 2021 @ ICSE) Chairs’ Welcome. IEEE, virtual event (30 May 2021), http://www.cs.ucl.ac.uk/staff/W.Langdon/icse2021/gi2021_message_from_the_chairs.pdf

  77. J.H. Moore, Is the evolution metaphor still necessary or even useful for genetic programming? Genetic Programming and Evolvable Machines Peer Commentary on the Thirtieth Anniversary of Genetic Programming: On the Programming of Computers by Means of Natural Selection

  78. C. Darwin, On the Origin of Species by Means of Natural Selection. John Murray, penguin classics, 1985 edn. (1859)

  79. W. Banzhaf et al., Genetic Programming – An Introduction; On the Automatic Evolution of Computer Programs and its Applications. Morgan Kaufmann, San Francisco, CA, USA (Jan 1998), https://www.amazon.co.uk/Genetic-Programming-Introduction-Artificial-Intelligence/dp/155860510X

  80. M. O’Neill, C. Ryan, Grammatical Evolution: Evolutionary Automatic Programming in a Arbitrary Language, Genetic programming, vol. 4. Kluwer Academic Publishers (2003), https://doi.org/10.1007/978-1-4615-0447-4

  81. J.F. Miller, (ed.): Cartesian Genetic Programming. Natural Computing Series, Springer (2011), https://doi.org/10.1007/978-3-642-17310-3

  82. J. Petke et al., Genetic improvement of software: a comprehensive survey. IEEE Trans. Evolut. Comput. 22(3), 415–432 (2018). https://doi.org/10.1109/TEVC.2017.2693219

    Article  Google Scholar 

  83. J. Petke et al., A survey of genetic improvement search spaces. In: Alexander, B., et al. (eds.) 7th edition of GI @ GECCO 2019. pp. 1715–1721. ACM, Prague, Czech Republic (Jul 13-17 2019), https://doi.org/10.1145/3319619.3326870

  84. W.B. Langdon, J. Petke, Software is not fragile. In: Parrend, P., et al. (eds.) Complex Systems Digital Campus E-conference, CS-DC’15. pp. 203–211. Proceedings in Complexity, Springer (Sep 30-Oct 1 2015), https://doi.org/10.1007/978-3-319-45901-1_24, invited talk

  85. W.B. Langdon, M. Harman, Optimising existing software with genetic programming. IEEE Trans. Evolut. Comput. 19(1), 118–135 (2015). https://doi.org/10.1109/TEVC.2013.2281544

    Article  Google Scholar 

  86. J. Petke, A. Brownlee, Software improvement with Gin: a case study. In: Nejati, S., Gay, G. (eds.) SSBSE 2019. LNCS, vol. 11664, pp. 183–189. Springer, Tallinn, Estonia (31 Aug - 1 Sep 2019), https://doi.org/10.1007/978-3-030-27455-9_14

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

    Article  Google Scholar 

  88. C.Y. Ishida, A.T.R. Pozo, GPSQL miner: SQL-grammar genetic programming in data mining. In: Fogel, D.B., et al. (eds.) Proceedings of the 2002 Congress on Evolutionary Computation CEC2002. pp. 1226–1231. IEEE Press (12-17 May 2002), https://doi.org/10.1109/CEC.2002.1004418

  89. J. Callan, J. Petke, Optimising SQL queries using genetic improvement. In: Petke, J., et al. (eds.) GI @ ICSE 2021. pp. 9–10. IEEE, internet (30 May 2021), https://doi.org/10.1109/GI52543.2021.00010

  90. E. Lukschandl et al., Automatic evolution of Java bytecode: First experience with the Java virtual machine. In: Poli, R., et al. (eds.) Late Breaking Papers at EuroGP’98: the First European Workshop on Genetic Programming. pp. 14–16. CSRP-98-10, The University of Birmingham, UK, Paris, France (14-15 Apr 1998), http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/csrp-98-10.pdf

  91. M. Orlov, M. Sipper, FINCH: A system for evolving Java (bytecode). In: Riolo, R., et al. (eds.) Genetic Programming Theory and Practice VIII, Genetic and Evolutionary Computation, vol. 8, chap. 1, pp. 1–16. Springer, Ann Arbor, USA (20-22 May 2010), https://doi.org/10.1007/978-1-4419-7747-2_1

  92. K. Yeboah-Antwi, B. Baudry, Embedding adaptivity in software systems using the ECSELR framework. In: Langdon, W.B., et al. (eds.) Genetic Improvement 2015 Workshop. pp. 839–844. ACM, Madrid (11-15 Jul 2015), https://doi.org/10.1145/2739482.2768425

  93. E. Schulte, et al., Automated program repair through the evolution of assembly code. In: Proceedings of the IEEE/ACM International Conference on Automated Software Engineering. pp. 313–316. ACM, Antwerp (20-24 Sep 2010), https://doi.org/10.1145/1858996.1859059

  94. W.B. Langdon et al., Genetic improvement of LLVM intermediate representation. In: Pappa, G., et al. (eds.) EuroGP 2023: Proceedings of the 26th European Conference on Genetic Programming. LNCS, vol. 13986, pp. 244–259. Springer Verlag, Brno, Czech Republic (12-14 Apr 2023), https://doi.org/10.1007/978-3-031-29573-7_16

  95. E. Schulte et al., Automated repair of binary and assembly programs for cooperating embedded devices. In: Proceedings of the eighteenth international conference on Architectural support for programming languages and operating systems. pp. 317–328. ASPLOS 2013, ACM, Houston, Texas, USA (Mar 16-20 2013), https://doi.org/10.1145/2451116.2451151

  96. H. Iba et al., Genetic programming with local hill-climbing. In: Davidor, Y., et al. (eds.) Parallel Problem Solving from Nature III. LNCS, vol. 866, pp. 334–343. Springer-Verlag, Jerusalem (9-14 Oct 1994), https://doi.org/10.1007/3-540-58484-6_274

  97. A.I. Esparcia-Alcazar, Genetic Programming for Adaptive Signal Processing. Ph.D. thesis, Electronics and Electrical Engineering, University of Glasgow, UK (Jul 1998), http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/esparcia-alcazar/thesis.ps.gz

  98. J. Lehman, K.O. Stanley, Novelty search and the problem with objectives. In: Riolo, R., et al. (eds.) Genetic Programming Theory and Practice IX, chap. 3, pp. 37–56. Genetic and Evolutionary Computation, Springer, Ann Arbor, USA (12-14 May 2011), https://doi.org/10.1007/978-1-4614-1770-5_3

  99. H. Rakotoarison, et al., Automated machine learning with Monte-Carlo Tree Search. In: Kraus, S. (ed.) Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI 2019. pp. 3296–3303. ijcai.org, Macao, China (Aug 10-16 2019), https://doi.org/10.24963/ijcai.2019/457

  100. C.G. Johnson, New directions in fitness evaluation: Commentary on Langdon’s JAWS30. Genetic Programming and Evolvable Machines Peer Commentary on the Thirtieth Anniversary of Genetic Programming: On the Programming of Computers by Means of Natural Selection

  101. S.W. Card, Towards an Information Theoretic Framework for Evolutionary Learning. Ph.D. thesis, Electrical Engineering and Computer Science, Syracuse University, USA (Aug 2011), https://surface.syr.edu/eecs_etd/307

  102. C.G. Johnson, Solving the Rubik’s cube with stepwise deep learning. Expert Syst. J. Knowl. Eng. (2021). https://doi.org/10.1111/exsy.12665

    Article  Google Scholar 

  103. X. Yao, Universal approximation by genetic programming. In: Haynes, T., et al. (eds.) Foundations of Genetic Programming. pp. 66–67. Orlando, Florida, USA (13 Jul 1999), http://www.cs.ucl.ac.uk/staff/W.Langdon/fogp/yao.ps.gz

  104. V. Parque, T. Miyashita, On vehicle surrogate learning with genetic programming ensembles. In: Cotta, C., et al. (eds.) GECCO ’18: Proceedings of the Genetic and Evolutionary Computation Conference Companion. pp. 1704–1710. ACM, Kyoto, Japan (2018), https://doi.org/10.1145/3205651.3208310

  105. K. Krawiec, Behavioral Program Synthesis with Genetic Programming, Studies in Computational Intelligence, vol. 618. Springer International Publishing (2015), https://doi.org/10.1007/978-3-319-27565-9

  106. W.B. Langdon, Directed crossover within genetic programming. Research Note RN/95/71, University College London, Gower Street, London WC1E 6BT, UK (Sep 1995), http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/directed_crossover.pdf

  107. F. Chicano et al., Dynastic potential crossover operator. Evolutionary Computation 30(3) (Fall 2022), https://doi.org/10.1162/evco_a_00305

  108. A.M. Zaidi, Accelerating control-flow intensive code in spatial hardware. Ph.D. thesis, Computer Laboratory, University of Cambridge (May 2015), https://www.cl.cam.ac.uk/techreports/UCAM-CL-TR-870.pdf, also available as Technical Report UCAM-CL-TR-870

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to W. B. Langdon.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Langdon, W.B. Response to comments on “Jaws 30”. Genet Program Evolvable Mach 24, 26 (2023). https://doi.org/10.1007/s10710-023-09474-y

Download citation

  • Published:

  • DOI: https://doi.org/10.1007/s10710-023-09474-y

Navigation