An extended probabilistic model building genetic network programming using both of good and bad individuals

2013 ◽  
Vol 8 (4) ◽  
pp. 339-347 ◽  
Author(s):  
Xianneng Li ◽  
Shingo Mabu ◽  
Kotaro Hirasawa
Keyword(s):  
Probabilistic Model ◽  
Model Building ◽  
Genetic Network ◽  
Network Programming ◽  
Genetic Network Programming

Genetic Network Programming with Estimation of Distribution Algorithms for Class Association Rule Mining in Traffic Prediction

2010 ◽  
Vol 14 (5) ◽  
pp. 497-509 ◽  
Author(s):  
Xianneng Li ◽  
◽  
Shingo Mabu ◽  
Huiyu Zhou ◽  
Kaoru Shimada ◽  
...  
Keyword(s):  
Association Rules ◽  
Probabilistic Model ◽  
Genetic Network ◽  
Traffic Prediction ◽  
Estimation Of Distribution Algorithms ◽  
Network Programming ◽  
Estimation Of Distribution ◽  
Prediction Systems ◽  
Distribution Algorithms ◽  
Genetic Network Programming

Genetic Network Programming (GNP) is one of the evolutionary optimization algorithms, which uses directed-graph structures to represent its solutions. It has been clarified that GNP works well to find class association rules in traffic prediction systems. In this paper, a novel evolutionary paradigm named GNP with Estimation of Distribution Algorithms (GNP-EDAs) is proposed and used to find important class association rules in traffic prediction systems. In GNP-EDAs, a probabilistic model replaces crossover and mutation to enhance the evolution. The new population of individuals is produced from the probabilistic distribution estimated from the selected elite individuals of the previous generation. The probabilistic information on the connections and transitions of GNP-EDAs is extracted from its population to construct the probabilistic model. In this paper, two methods are described to build the probabilistic model for producing the offspring. In addition, a classification mechanism is introduced to estimate the traffic prediction based on the extracted class association rules. We compared GNPEDAs with the conventional GNP and the simulation results showed that GNP-EDAs can extract the class association rules more effectively, when the number of the candidate class association rules increase. And the classification accuracy of the proposed method shows good results in traffic prediction systems.

Co-evolution of Hetero Multiagent Systems using Genetic Network Programming.

2003 ◽  
Vol 123 (3) ◽  
pp. 544-551 ◽  
Author(s):  
Kotaro Hirasawa ◽  
Masafumi Okubo ◽  
Jinglu Hu ◽  
Junichi Murata ◽  
Yuko Matsuya
Keyword(s):  
Multiagent Systems ◽  
Genetic Network ◽  
Network Programming ◽  
Genetic Network Programming

Buying and Selling Stocks of Multi Brands Using Genetic Network Programming with Control Nodes

2008 ◽  
Vol 128 (12) ◽  
pp. 1811-1819 ◽  
Author(s):  
Etsushi Ohkawa ◽  
Yan Chen ◽  
Zhiguo Bao ◽  
Shingo Mabu ◽  
Kaoru Shimada ◽  
...  
Keyword(s):  
Genetic Network ◽  
Network Programming ◽  
Genetic Network Programming
Sign in / Sign up

Export Citation Format

Share Document