Improved sampling using loopy belief propagation for probabilistic model building genetic programming

Sato, Hiroyuki, Hasegawa, Yoshihiko, Bollegala, Danushka ORCID: 0000-0003-4476-7003 and Iba, Hitoshi (2015) Improved sampling using loopy belief propagation for probabilistic model building genetic programming. SWARM AND EVOLUTIONARY COMPUTATION, 23. pp. 1-10.

Text Sato_2015.pdf - Unspecified Access to this file is embargoed until Unspecified. Download (1MB)

Abstract

In recent years, probabilistic model building genetic programming (PMBGP) for program optimization 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 behaviors of LBP. We find that POLE-BP shows better search performance with some problems because LBP boosts the generation of building blocks.

Item Type:	Article
Uncontrolled Keywords:	Genetic programming, Estimation of distribution algorithms, Loopy belief propagation, Probabilistic model building GP
Subjects:	?? QA75 ??
Depositing User:	Symplectic Admin
Date Deposited:	23 Nov 2015 10:16
Last Modified:	15 Dec 2022 12:06
DOI:	10.1016/j.swevo.2015.02.002
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/2038381