Paper Info
Title
Improving Gene Expression Programming Using Parallel Taboo Search
Abstract
By the strategy of exerting advantage and avoiding disadvantage respectively, hybrid GEP (gene expression programming) algorithm includes many advantages, which can effectively improve the efficiency of GEP and offer more effective solution to the difficult problems of technology and engineering fields. This paper proposes GEP-PTS algorithm, which combines simple GEP and PTS. In GEP-PTS, we propose parallel taboo search (PTS) based on simple taboo search to conduct local search. Extensive experiments show that the accuracy of function model found by GEP-PTS is improved 4.19%-7.93% compared with simple GEP.
Year
DOI
Venue
2009
10.1109/ICNC.2009.124
ICNC (3)
Keywords
Field
DocType
parallel taboo search,evolutionary computation,gep-pts algorithm,improving gene expression programming,mathematical programming,simple gep,hybrid gep,search problems,difficult problem,effective solution,local search,gene expression programming,engineering field,simple taboo search,accuracy,algorithm design and analysis,functional model,data mining,gene expression,productivity
Gene expression programming,Mathematical optimization,Algorithm design,Computer science,Evolutionary computation,Function model,Artificial intelligence,Local search (optimization),Programming profession,Taboo,Machine learning
Conference
Volume
ISBN
Citations 
3
978-0-7695-3736-8
0
PageRank 
References 
Authors
0.34
5
3
Name
Order
Citations
PageRank
Yuan Rao16210.81
Ruchuan Wang24613.37
Chang-an Yuan3859.88