Investigating the parameter space of evolutionary algorithms
Created by W.Langdon from
gp-bibliography.bib Revision:1.8010
- @Article{Sipper2018,
-
author = "Moshe Sipper and Weixuan Fu and Karuna Ahuja and
Jason H. Moore",
-
title = "Investigating the parameter space of evolutionary
algorithms",
-
journal = "BioData Mining",
-
year = "2018",
-
volume = "11",
-
number = "1",
-
month = "17 " # feb,
-
keywords = "genetic algorithms, genetic programming, evolutionary
algorithms, Meta-genetic algorithm, Parameter tuning,
Hyper-parameter",
-
ISSN = "1756-0381",
-
DOI = "doi:10.1186/s13040-018-0164-x",
-
size = "14 pages",
-
abstract = "Evolutionary computation (EC) has been widely applied
to biological and biomedical data. The practice of EC
involves the tuning of many parameters, such as
population size, generation count, selection size, and
crossover and mutation rates. Through an extensive
series of experiments over multiple evolutionary
algorithm implementations and 25 problems we show that
parameter space tends to be rife with viable
parameters, at least for the problems studied herein.
We discuss the implications of this finding in practice
for the researcher employing EC.",
- }
Genetic Programming entries for
Moshe Sipper
Weixuan Fu
Karuna Ahuja
Jason H Moore
Citations