DocumentCode :
2860197
Title :
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
Link To Document :
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