DocumentCode
3014775
Title
Improved Genetic Programming Algorithm
Author
Cheng, Huifang ; Zhang, Yongqiang ; Li, Fangping
Author_Institution
Sch. of Inf. & Electr. Eng., Hebei Univ. of Eng., Handan, China
fYear
2009
fDate
8-9 Dec. 2009
Firstpage
168
Lastpage
171
Abstract
The present study aims at improving the problem solving ability of the canonical genetic programming algorithm. The proposed method can be described as follows. The first investigates initialising population, the second investigates reproduction operator, the third investigates crossover operator, the fourth investigates mutation operation. This approach is examined on two experiments about symbolic regression. The results suggest that the new approach is more effective and more efficient than the canonical one.
Keywords
genetic algorithms; regression analysis; canonical genetic programming algorithm; crossover operator; mutation operation; problem solving; reproduction operator; symbolic regression; Asia; Convergence; Educational institutions; Genetic algorithms; Genetic engineering; Genetic mutations; Genetic programming; Problem-solving; Random number generation; Wheels; Convergence; Genetic Programming; Novel method; Operator;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Interaction and Affective Computing, 2009. ASIA '09. International Asia Symposium on
Conference_Location
Wuhan
Print_ISBN
978-0-7695-3910-2
Electronic_ISBN
978-1-4244-5406-8
Type
conf
DOI
10.1109/ASIA.2009.39
Filename
5376006
Link To Document