eprintid: 10074376
rev_number: 21
eprint_status: archive
userid: 608
dir: disk0/10/07/43/76
datestamp: 2019-05-20 16:37:18
lastmod: 2021-10-11 22:45:43
status_changed: 2019-05-20 16:37:18
type: proceedings_section
metadata_visibility: show
creators_name: Petke, J
creators_name: Langdon, W
title: Genetic Improvement of Data gives Binary Logarithm from sqrt
ispublished: pub
divisions: UCL
divisions: B04
divisions: C05
divisions: F48
keywords: genetic programming, GI, search based software engineering, SBSE, software maintenance of empirical constants, data transplantation
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
abstract: Automated search in the form of the Covariance Matrix Adaptation
Evolution Strategy (CMA-ES), plus manual code changes, transforms 512 Newton-Raphson floating point start numbers from an
open source GNU C library, glibc, table driven square root function
to create a new bespoke custom mathematical implementation of
double precision binary logarithm log2 for C in seconds.
date: 2019-07
date_type: published
publisher: Association for Computing Machinery (ACM)
official_url: http://dx.doi.org/10.1145/3319619.3321954
oa_status: green
full_text_type: other
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1658739
doi: 10.1145/3319619.3321954
lyricists_name: Langdon, William
lyricists_name: Petke, Justyna
lyricists_id: WBLAN93
lyricists_id: JPETK66
actors_name: Petke, Justyna
actors_id: JPETK66
actors_role: owner
full_text_status: public
series: Genetic and Evolutionary Computation Conference (GECCO)
publication: Genetic and Evolutionary Computation Conference
volume: 2019
place_of_pub: New York, NY, USA
pagerange: 413-414
event_title: 2019 Genetic and Evolutionary Computation Conference (GECCO)
event_location: Prague, Czech Republic
institution: Genetic and Evolutionary Computation Conference
book_title: Proceedings of the 2019 Genetic and Evolutionary Computation Conference (GECCO)
citation:        Petke, J;    Langdon, W;      (2019)    Genetic Improvement of Data gives Binary Logarithm from sqrt.                     In:  Proceedings of the 2019 Genetic and Evolutionary Computation Conference (GECCO).  (pp. pp. 413-414).  Association for Computing Machinery (ACM): New York, NY, USA.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10074376/1/pos179s1-file1.pdf