eprintid: 10146198
rev_number: 9
eprint_status: archive
userid: 699
dir: disk0/10/14/61/98
datestamp: 2022-04-01 17:22:54
lastmod: 2023-08-30 09:37:02
status_changed: 2022-04-01 17:22:54
type: article
metadata_visibility: show
sword_depositor: 699
creators_name: Langdon, WB
title: Genetic programming convergence
ispublished: pub
divisions: C05
divisions: F48
divisions: B04
divisions: UCL
keywords: Evolutionary computation, stochastic search, diversity, bottom up
incremental evaluation, PIE, propagation, infection, and execution, SIMD parallel processing, AVX vector instructions
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
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 within existing memory use.
date: 2021-03
date_type: published
publisher: Springer Verlag
official_url: https://doi.org/10.1007/s10710-021-09405-9
oa_status: green
full_text_type: other
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1886782
doi: 10.1007/s10710-021-09405-9
lyricists_name: Langdon, William
lyricists_id: WBLAN93
actors_name: Langdon, William
actors_id: WBLAN93
actors_role: owner
funding_acknowledgements: EP/M025853/1 [EPSRC]; EP/P005888/1 [EPSRC]
full_text_status: public
publication: Genetic Programming and Evolvable Machines
volume: 23
pagerange: 71-104
pages: 34
citation:        Langdon, WB;      (2021)    Genetic programming convergence.                   Genetic Programming and Evolvable Machines , 23    pp. 71-104.    10.1007/s10710-021-09405-9 <https://doi.org/10.1007/s10710-021-09405-9>.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10146198/1/langdon_GPEM_gpconv.pdf