On Using Surrogates with Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{Hildebrandt:2014:EC,
-
author = "Torsten Hildebrandt and Juergen Branke",
-
title = "On Using Surrogates with Genetic Programming",
-
journal = "Evolutionary Computation",
-
year = "2015",
-
volume = "23",
-
number = "3",
-
pages = "343--367",
-
month = "Fall",
-
keywords = "genetic algorithms, genetic programming, surrogates,
phenotypic characterization, ECJ",
-
ISSN = "1063-6560",
-
DOI = "doi:10.1162/EVCO_a_00133",
-
size = "25 pages",
-
abstract = "One way to accelerate evolutionary algorithms with
expensive fitness evaluations is to combine them with
surrogate models. Surrogate models are efficiently
computable approximations of the fitness function,
derived by means of statistical or machine learning
techniques from samples of fully evaluated solutions.
But these models usually require a numerical
representation, and therefore can not be used with the
tree representation of Genetic Programming (GP). In
this paper, we present a new way to use surrogate
models with GP. Rather than using the genotype directly
as input to the surrogate model, we propose using a
phenotypic characterisation. This phenotypic
characterization can be computed efficiently and allows
us to define approximate measures of equivalence and
similarity. Using a stochastic, dynamic job shop
scenario as an example of simulation-based GP with an
expensive fitness evaluation, we show how these ideas
can be used to construct surrogate models and improve
the convergence speed and solution quality of GP.",
-
notes = "Posted Online June 26, 2014",
- }
Genetic Programming entries for
Torsten Hildebrandt
Jurgen Branke
Citations