Tag-based Module Regulation for Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7964

@InProceedings{lalejini:2022:GECCOhop,

• author = "Alexander Lalejini and Matthew Moreno and Charles Ofria",
• title = "Tag-based Module Regulation for Genetic Programming",
• booktitle = "Proceedings of the 2022 Genetic and Evolutionary Computation Conference Companion",
• year = "2022",
• editor = "Marcus Gallagher",
• pages = "25--26",
• address = "Boston, USA",
• series = "GECCO '22",
• month = "9-13 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, tag-based referencing, automatic program synthesis, gene regulation, SignalGP",
• isbn13 = "978-1-4503-9268-6/22/07",
• DOI = "doi:10.1145/3520304.3534060",
• video_url = "https://vimeo.com/721571770",
• abstract = "This Hot-off-the-Press paper summarizes our recently published work, {"}Tag-based regulation of modules in genetic programming improves context-dependent problem solving,{"} published in Genetic Programming and Evolvable Machines [1]. We introduce and experimentally demonstrate tag-based genetic regulation, a genetic programming (GP) technique that allows programs to dynamically adjust which code modules to express. Tags are evolvable labels that provide a flexible naming scheme for referencing code modules. Tag-based regulation extends tag-based naming schemes to allow programs to {"}promote{"} and {"}repress{"} code modules to alter module execution patterns. We find that tag-based regulation improves problem-solving success on problems where programs must adjust how they respond to current inputs based on prior inputs; indeed, some of these problems could not be solved until regulation was added. We also identify scenarios where the correct response to an input does not change over time, rendering tag-based regulation an unnecessary functionality that can sometimes impede evolution. Broadly, tag-based regulation adds to our repertoire of techniques for evolving more dynamic computer programs and can easily be incorporated into existing tag-enabled GP systems.",
• notes = "GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Alexander Lalejini Matthew Andres Moreno Charles Ofria

Citations