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
         
        
        
        
        
        
            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;
         
        
        
        
            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
         
        
        
            DOI : 
10.1109/ASIA.2009.39