Feature Encapsulation by Stages Using Grammatical Evolution

Created by W.Langdon from gp-bibliography.bib Revision:1.8010

@InProceedings{reyes-fernandez-de-bulnes:2024:GECCOcomp,

• author = "Darian {Reyes Fernandez De Bulnes} and Allan {De Lima} and Aidan Murphy and Douglas {Mota Dias} and Conor Ryan",
• title = "Feature Encapsulation by Stages Using Grammatical Evolution",
• booktitle = "Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion",
• year = "2024",
• editor = "Ting Hu and Aniko Ekart",
• pages = "531--534",
• address = "Melbourne, Australia",
• series = "GECCO '24",
• month = "14-18 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, grammatical evolution, feature encapsulation, multi-target, multioutput: Poster",
• isbn13 = "979-8-4007-0495-6",
• DOI = "doi:10.1145/3638530.3654097",
• size = "4 pages",
• abstract = "This paper introduces a novel mechanism, Feature Encapsulation by Stages (FES), to encapsulate and transfer features as knowledge in a staged manner within the evolutionary process. Encapsulation happens via input space expansion in one or more stages by adding the best-of-run individual as an additional input. This input space expansion is managed by augmenting the grammar. We study the feasibility of dynamically modifying the grammar and reinitialising the population to make way for new individuals which quickly evolve to a better fitness level. Five different approaches to stage management are examined. In addition, three different selection processes, namely, Tournament, Lexicase and Lexi2, are used to investigate which is best suited to use with our encapsulation procedure. We benchmark our procedure on two problem domains, Boolean and Classification, and demonstrate these staging strategies lead to significantly better results. Statistical tests show our FES outperforms the standard baseline in all Boolean problems, with a 4-stage version performing best, obtaining significant differences in all Boolean problems.",
• notes = "GECCO-2024 GP A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Darian Reyes Fernandez de Bulnes Allan De Lima Aidan Murphy Douglas Mota Dias Conor Ryan

Citations