- author = "Anil Kumar Saini and Lee Spector",
- title = "{GLEAM}: Genetic Learning by Extraction and Absorption of Modules",
- booktitle = "Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion",
- year = "2021",
- editor = "Francisco Chicano and Alberto Tonda and Krzysztof Krawiec and Marde Helbig and Christopher W. Cleghorn and Dennis G. Wilson and Georgios Yannakakis and Luis Paquete and Gabriela Ochoa and Jaume Bacardit and Christian Gagne and Sanaz Mostaghim and Laetitia Jourdan and Oliver Schuetze and Petr Posik and Carlos Segura and Renato Tinos and Carlos Cotta and Malcolm Heywood and Mengjie Zhang and Leonardo Trujillo and Risto Miikkulainen and Bing Xue and Aneta Neumann and Richard Allmendinger and Fuyuki Ishikawa and Inmaculada Medina-Bulo and Frank Neumann and Andrew M. Sutton",
- pages = "263--264",
- address = "internet",
- series = "GECCO '21",
- month = jul # " 10-14",
- organisation = "SIGEVO",
- publisher = "Association for Computing Machinery",
- publisher_address = "New York, NY, USA",
- keywords = "genetic algorithms, genetic programming, modularity, PushGP, program synthesis, tag-based reference: Poster",
- isbn13 = "978-1-4503-8351-6",
-
DOI = "
doi:10.1145/3449726.3459544",
- size = "2 pages",
- abstract = "Efforts to evolve modularity in genetic programming are as old as the field itself. While many techniques have been proposed to evolve programs that make use of simultaneously-evolved modules, the general problem of evolving large-scale, modular software still stands as a central challenge to the field. In this paper, we present GLEAM, a mechanism of achieving modularity, whereby an evolving program uses a local library of modules. The program can refer to the modules during execution and the ones not used by the program are eventually replaced by code segments extracted from the program itself. Modules can also be absorbed by the program when their references in the program get expanded to the full code segments. We show that this simple mechanism improves the success rate on a number of program synthesis problems.",
-
notes = "University of Massachusetts Amherst
GECCO-2021 A Recombination of the 30th International Conference on Genetic Algorithms (ICGA) and the 26th Annual Genetic Programming Conference (GP)",
