- author = "William B. Langdon and Afnan Al-Subaihin and David Clark",
- title = "Measuring Failed Disruption Propagation in Genetic Programming",
- booktitle = "Proceedings of the 2022 Genetic and Evolutionary Computation Conference",
- year = "2022",
- editor = "Alma Rahat and Jonathan Fieldsend and Markus Wagner and Sara Tari and Nelishia Pillay and Irene Moser and Aldeida Aleti and Ales Zamuda and Ahmed Kheiri and Erik Hemberg and Christopher Cleghorn and Chao-li Sun and Georgios Yannakakis and Nicolas Bredeche and Gabriela Ochoa and Bilel Derbel and Gisele L. Pappa and Sebastian Risi and Laetitia Jourdan and Hiroyuki Sato and Petr Posik and Ofer Shir and Renato Tinos and John Woodward and Malcolm Heywood and Elizabeth Wanner and Leonardo Trujillo and Domagoj Jakobovic and Risto Miikkulainen and Bing Xue and Aneta Neumann and Richard Allmendinger and Inmaculada Medina-Bulo and Slim Bechikh and Andrew M. Sutton and Pietro Simone Oliveto",
- pages = "964--972",
- 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, information
theory, software resilience, SBSE, antifragile
correctness attraction, entropy, mutational
robustness,
entropy loss, Failed Disruption Propagation,
GP, Fitness Landscapes/Distances, Genotype-phenotype mapping
Data processing inequality",
- isbn13 = "978-1-4503-9237-2",
-
URL = "
http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/langdon_2022_GECCO2.pdf",
-
URL = "
https://discovery.ucl.ac.uk/id/eprint/10154055/",
-
DOI = "
doi:10.1145/3512290.3528738",
-
video_url = "https://vimeo.com/723872914",
-
video_url = "https://youtu.be/aQDZnP_-mGE",
-
code_url = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/gp-code/GPfdp.tar.gz",
- size = "9 pages",
- abstract = "Information theory explains the robustness of deep GP trees, with on average up to 83.3 percent of crossover run time disruptions failing to propagate to the root node, and so having no impact on fitness, leading to phenotypic convergence. Monte Carlo simulations of perturbations covering the whole tree demonstrate a model based on random synchronisation of the evaluation of the parent and child which cause parent and offspring evaluations to be identical. This predicts the effectiveness of fitness measurement grows slowly as O(log(n)) with number n of test cases. This geometric distribution model is tested on genetic programming symbolic regression.",
-
notes = "ACM Paper #: 12477.51
Slides: http://www.cs.ucl.ac.uk/staff/W.Langdon/gecco2022/langdon_2022_GECCO2_slides.pdf
GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)",
