- author = "William B. Langdon",
- title = "Genetic Programming Convergence [Hot of the Press]",
- booktitle = "GECCO 2022 Hot off the Press",
- year = "2022",
- editor = "Marcus Gallagher",
- pages = "27--28",
- 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",
- isbn13 = "978-1-4503-9268-6/22/07",
- acmid = "12519.409",
-
URL = "
http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/langdon_2022_GECCOhop_gpem.pdf",
-
DOI = "
10.1145/3520304.3534063",
-
video_url = "
https://vimeo.com/723873152",
-
video_url = "https://youtu.be/irLoaq6MzbU",
- size = "2 pages",
- abstract = "We study both genotypic and phenotypic convergence in GP floating point continuous domain symbolic regression over thousands of generations. Subtree fitness variation across the population is measured and shown in many cases to fall. In an expanding region about the root node, both genetic opcodes and function evaluation values are identical or nearly identical. Bottom up (leaf to root) analysis shows both syntactic and semantic (including entropy) similarity expand from the outermost node. Despite large regions of zero variation, fitness continues to evolve and near zero crossover disruption suggests improved GP systems. W.B. Langdon. 2022. Genetic Programming Convergence GP & EM 23,1, 71-104. http://dx.doi.org/10.1007/s10710-021-09405-9",
-
notes = "Summary of \cite{langdon:GPEM:gpconv} Slides:
http://www.cs.ucl.ac.uk/staff/W.Langdon/gecco2022/langdon_2022_GECCOhop_gpem_slides.pdf
GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)",
