Improved sampling using loopy belief propagation for probabilistic model building genetic programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{Sato:2015:SEC,
-
author = "Hiroyuki Sato and Yoshihiko Hasegawa and
Danushka Bollegala and Hitoshi Iba",
-
title = "Improved sampling using loopy belief propagation for
probabilistic model building genetic programming",
-
journal = "Swarm and Evolutionary Computation",
-
year = "2015",
-
volume = "23",
-
pages = "1--10",
-
month = aug,
-
keywords = "genetic algorithms, genetic programming, Estimation of
distribution algorithms, Loopy belief propagation,
Probabilistic model building GP",
-
ISSN = "2210-6502",
-
URL = "http://danushka.net/papers/Sato_2015.pdf",
-
DOI = "doi:10.1016/j.swevo.2015.02.002",
-
URL = "http://www.sciencedirect.com/science/article/pii/S2210650215000176",
-
size = "10 pages",
-
abstract = "In recent years, probabilistic model building genetic
programming (PMBGP) for program optimisation has
attracted considerable interest. PMBGPs generally use
probabilistic logic sampling (PLS) to generate new
individuals. However, the generation of the most
probable solutions (MPSs), i.e., solutions with the
highest probability, is not guaranteed. In the present
paper, we introduce loopy belief propagation (LBP) for
PMBGPs to generate MPSs during the sampling process. We
selected program optimization with linkage estimation
(POLE) as the foundation of our approach and we refer
to our proposed method as POLE-BP. We apply POLE-BP and
existing methods to three benchmark problems to
investigate the effectiveness of LBP in the context of
PMBGPs, and we describe detailed examinations of the
behaviours of LBP. We find that POLE-BP shows better
search performance with some problems because LBP
boosts the generation of building blocks.",
- }
Genetic Programming entries for
Hiroyuki Sato
Yoshihiko Hasegawa
Danushka Bollegala
Hitoshi Iba
Citations