An Improved GAPSO Hybrid Programming Algorithm

Author

Wu, Xiaojun ; Wang, Ying ; Zhang, Tiantian

Author_Institution

Sch. of Autom., Northwestern Polytech. Univ., Xi´´an, China

fYear

2009

fDate

19-20 Dec. 2009

Firstpage

1

Lastpage

4

Abstract

GAPSO hybrid programming algorithm, which is a concise, effective and stable algorithm to solve the hierarchical problem based on GP algorithm. In terms of the specific characteristics of discrete magnitude and continuous magnitude, as well as the superiority of PSO in continuous quantity optimization, in this paper we propose an improved algorithm, which optimizes continuous magnitude by PSO while using GP for discrete magnitude optimization. Then through mass contrast experiments with GAPSO hybrid programming algorithm, we could see that Improved GAPSO hybrid programming algorithm is more stable and effective in function modeling.

Keywords

genetic algorithms; mathematical programming; particle swarm optimisation; GP algorithm; continuous magnitude; continuous quantity optimization; discrete magnitude; function modeling; hierarchical problem; improved GAPSO hybrid programming; mass contrast experiments; Automatic programming; Automation; Binary trees; Biology computing; Functional programming; Genetic programming; Learning; Particle swarm optimization; Predictive models; Tree data structures;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Engineering and Computer Science, 2009. ICIECS 2009. International Conference on

Conference_Location

Wuhan

Print_ISBN

978-1-4244-4994-1

Type

conf

DOI

10.1109/ICIECS.2009.5365983

Filename

5365983