- author = "Ryan Boldi and Ashley Bao and Martin Briesch and Thomas Helmuth and Dominik Sobania and Lee Spector and Alexander Lalejini",
- title = "Untangling the Effects of Down-Sampling and Selection in Genetic Programming",
- booktitle = "ALIFE 2024: Proceedings of the 2024 Artificial Life Conference",
- year = "2024",
- editor = "Andres Faina and Sebastian Risi and Eric Medvet and Kasper Stoy and Bert Chan and Karine Miras and Payam Zahadat and Djordje Grbic and Giorgia Nadizar",
- pages = "705--716",
- address = "Copenhagen",
- month = jul # " 22-26",
- organization = "The International Society for Artificial Life",
- publisher = "MIT Press",
- keywords = "genetic algorithms, genetic programming, lexicase, PushGP, program synthesis benchmark problems, tournament, fitness proportional selection, mplicit Fitness Sharing, IFS, diversity maintenance",
-
URL = "
https://direct.mit.edu/isal/proceedings/isal2024/36/88/123536",
-
URL = "
https://direct.mit.edu/isal/proceedings-pdf/isal2024/36/88/2461089/isal_a_00832.pdf",
-
DOI = "
doi:10.1162/isal_a_00832",
-
code_url = "https://github.com/lspector/propeller",
- size = "12 pages",
- abstract = "the lexicase parent selection algorithm. We test whether these down-sampling techniques can also improve problem-solving success in the context of three other commonly used selection methods, fitness-proportionate, tournament, implicit fitness sharing plus tournament selection, across six program synthesis GP problems. We verified that down-sampling can significantly improve the problem-solving success for all three of these other selection schemes, demonstrating its general efficacy. We discern that the selection pressure imposed by the selection scheme does not interact with the down-sampling method. However, we find that informed down-sampling can improve problem solving success significantly over random down-sampling when the selection scheme has a mechanism for diversity maintenance like lexicase or implicit fitness sharing. Overall, our results suggest that down-sampling should be considered more often when solving test-based problems, regardless of the selection scheme in use.",
-
notes = "'we expand
on...'\cite{boldi:2023:GECCOcomp}
'tournament sizes of t= 2,5,10,30'. IFS tournament size = 30. 'farthest first traversal (Hochbaum and Shmoys, 1985)'
Count Odds, Fizz Buzz, Scrabble Score, Small or Large, Fuel Cost, Middle Character. Turing complete instruction set...looping and conditional execution.
Diversity maintenance. Selection Pressure.
