Abstract
General methods for the evolution of programs with modular structure have long been sought by genetic programming researchers, in part because modularity has long been considered to be essential, or at least helpful, for human programmers when they develop large-scale software projects. Multiple efforts have been made in this direction, and while success has been demonstrated in specific contexts, no general scheme has yet been demonstrated to provide benefits for evolutionary program synthesis that are similar in generality and significance to those provided by modularity in human software engineering. In this chapter, we present and analyze a new framework for the study of the evolution of modularity, called GLEAM (Genetic Learning by Extraction and Absorption of Modules). GLEAM’s flexible architecture and tunable parameters allow researchers to test different methods related to the generation, propagation, and use of modules in genetic programming.
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Angeline, P.J., Pollack, J.B.: The evolutionary induction of subroutines. In: Proceedings of the 14th Annual Conference of the Cognitive Science Society, pp. 236–241. Bloomington, Indiana (1992)
Banzhaf, W., Banscherus, D., Dittrich, P.: Hierarchical genetic programming using local modules. In: Bar-Yam, Y., Minai, A. (eds.) Unifying Themes in Complex Systems - Proceedings of 2nd International Conference on Complex Systems, pp. 321–330. CRC Press (2018)
Helmuth, T., Abdelhady, A.: Benchmarking parent selection for program synthesis by genetic programming. In: Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, pp. 237–238 (2020)
Helmuth, T., Spector, L.: General program synthesis benchmark suite. In: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, pp. 1039–1046 (2015)
Keijzer, M., Ryan, C., Cattolico, M.: Run transferable libraries - learning functional bias in problem domains. In: Genetic and Evolutionary Computation Conference, pp. 531–542. Springer (2004)
Kelly, S., Newsted, J., Banzhaf, W., Gondro, C.: A modular memory framework for time series prediction. In: Proceedings of the 2020 Genetic and Evolutionary Computation Conference, pp. 949–957 (2020)
Koza, J., Bennet, F., Andre, D., Keane, M.: Genetic Programming III. Morgan Kaufmann Publishers (1999)
Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press (1992)
Lalejini, A., Ofria, C.: Evolving event-driven programs with signalgp. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 1135–1142 (2018)
O’Neill, M., Spector, L.: Automatic programming: the open issue? Genet Progr. Evol. Mach. 20, 1–12 (2019)
Spector, L.: Evolving control structures with automatically defined macros. In: Working Notes of the AAAI Fall Symposium on Genetic Programming, pp. 99–105 (1995)
Spector, L., Klein, J., Keijzer, M.: The push3 execution stack and the evolution of control. In: Proceedings of the 7th Annual Conference on Genetic and Evolutionary Computation, pp. 1689–1696 (2005)
Spector, L., Martin, B., Harrington, K., Helmuth, T.: Tag-based modules in genetic programming. In: Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, pp. 1419–1426 (2011)
Spector, L., Robinson, A.: Genetic programming and autoconstructive evolution with the push programming language. Genet Progr. Evol. Mach. 3(1), 7–40 (2002)
Swafford, J.M., Hemberg, E., O’Neill, M., Brabazon, A.: Analyzing module usage in grammatical evolution. In: International Conference on Parallel Problem Solving from Nature, pp. 347–356. Springer (2012)
Walker, J.A., Miller, J.F.: The automatic acquisition, evolution and reuse of modules in cartesian genetic programming. IEEE Trans. Evol. Comput. 12(4), 397–417 (2008)
Acknowledgements
This material is based upon work supported by the National Science Foundation under Grant No. 1617087. Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the National Science Foundation. This work was performed in part using high performance computing equipment obtained under a grant from the Collaborative R&D Fund managed by the Massachusetts Technology Collaborative.
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Saini, A.K., Spector, L. (2022). Evolving and Analyzing Modularity with GLEAM (Genetic Learning by Extraction and Absorption of Modules). In: Banzhaf, W., Trujillo, L., Winkler, S., Worzel, B. (eds) Genetic Programming Theory and Practice XVIII. Genetic and Evolutionary Computation. Springer, Singapore. https://doi.org/10.1007/978-981-16-8113-4_10
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