Models of Performance of Evolutionary Program Induction Algorithms Based on Indicators of Problem Difficulty
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @Article{Graff:2013:EC,
-
author = "Mario Graff and Riccardo Poli and Juan J. Flores",
-
title = "Models of Performance of Evolutionary Program
Induction Algorithms Based on Indicators of Problem
Difficulty",
-
journal = "Evolutionary Computation",
-
year = "2013",
-
volume = "21",
-
number = "4",
-
pages = "533--560",
-
month = "Winter",
-
keywords = "genetic algorithms, genetic programming, Evolutionary
program-induction algorithms, performance forecasting,
hardness measures, wind speed forecasting",
-
ISSN = "1063-6560",
-
DOI = "doi:10.1162/EVCO_a_00096",
-
size = "28 pages",
-
abstract = "Modelling the behaviour of algorithms is the realm of
Evolutionary Algorithm theory. From a practitioner's
point of view, theory must provide some guidelines
regarding which algorithm/parameters to use in order to
solve a particular problem. Unfortunately, most
theoretical models of evolutionary algorithms are
difficult to apply to realistic situations. However, in
recent work (Graff and Poli, 2008, 2010), where we
developed a method to practically estimate the
performance of evolutionary program induction
algorithms (EPAs), we stated addressing this issue. The
method was quite general; however, it suffered from
some limitations: it required the identification of a
set of reference problems, it required hand picking a
distance measure in each particular domain, and the
resulting models were opaque being typically linear
combinations of one hundred features or more. In this
paper, we propose a significant improvement of this
technique that overcomes the three limitations of our
previous method. We achieve this through the use of a
novel set of features for assessing problem difficulty
for EPAs which are very general, essentially being
based on the notion of finite difference. To show the
capabilities or our technique and compare it with our
previous performance models, we create models for the
same two important classes of problems - symbolic
regression on rational functions and Boolean function
induction - used in our previous work. We model a
variety of EPAs. The comparison showed that for the
majority of the algorithms and problem classes, the new
method produced much simpler and more accurate models
than before. To further illustrate the practicality of
the technique and its generality (beyond EPAs), we have
also used it to predict the performance of both
auto-regressive models and EPAs on the problem of wind
speed forecasting, obtaining simpler and accurate
models that outperform in all cases our previous
performance models.",
-
notes = "Posted online on 8 Nov 2012. Cited by \cite{Graff2013}
Neurocomputing, doi:10.1016/j.neucom.2013.05.035",
- }
Genetic Programming entries for
Mario Graff Guerrero
Riccardo Poli
Juan J Flores
Citations