scholarly journals Revisiting Genetic Network Programming (GNP): Towards the Simplified Genetic Operators

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 43274-43289 ◽  
Author(s):  
Xianneng Li ◽  
Huiyan Yang ◽  
Meihua Yang
Keyword(s):  
Genetic Network ◽  
Network Programming ◽  
Genetic Operators ◽  
Genetic Network Programming

Alternate Genetic Network Programming with Association Rules Acquisition Mechanisms Between Attribute Families

2006 ◽  
Vol 10 (6) ◽  
pp. 954-963 ◽  
Author(s):  
Kaoru Shimada ◽  
◽  
Kotaro Hirasawa ◽  
Jinglu Hu
Keyword(s):  
Association Rules ◽  
Genetic Network ◽  
Rule Extraction ◽  
Optimization Techniques ◽  
Network Programming ◽  
Genetic Operators ◽  
Chi Squared ◽  
Evolutionary Optimization Techniques ◽  
Rules Acquisition ◽  
Genetic Network Programming

A method of association rule mining with chi-squared test using Alternate Genetic Network Programming (aGNP) is proposed. GNP is one of the evolutionary optimization techniques, which uses directed graph structures as genes. aGNP is an extended GNP in terms of including two kinds of node function sets. The proposed system can extract important association rules whose antecedent and consequent are composed of the attributes of each family defined by users. Rule extraction is done without identifying frequent itemsets used in Apriori-like methods. The method can be applied to rule extraction from dense database, and can extract dependent pairs of the sets of attributes in the database. Extracted rules are stored in a pool all together through generations and reflected in genetic operators as acquired information. In this paper, we describe the algorithm capable of finding the important association rules and present some experimental results.

scholarly journals Adaptation and Self-Adaptation Mechanisms in Genetic Network Programming for Mining Association Rules

2007 ◽  
Vol 11 (3) ◽  
pp. 343-353 ◽  
Author(s):  
Karla Taboada ◽  
◽  
Eloy Gonzales ◽  
Kaoru Shimada ◽  
Shingo Mabu ◽  
...  
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Directed Graphs ◽  
Genetic Network ◽  
Network Programming ◽  
Genetic Operators ◽  
Mutation Operators ◽  
Self Adaptation ◽  
Crossover And Mutation ◽  
Genetic Network Programming

In this paper we propose a method of association rule mining using Genetic Network Programming (GNP) with adaptive and self-adaptive mechanisms of genetic operators in order to improve the performance of association rule extraction systems. GNP is one of the evolutionary methods, whose directed graphs are evolved to find a solution as individuals. Adaptation behavior in GNP is related to adjust the setting of control parameters such as the proportion of crossover and mutation. The aim is not only to find suitable adjustments but to do it efficiently. Regarding to self-adaptation, the algorithm controls the setting of these parameters themselves – embedding them into an individual’s genome and evolving them, and it usually changes the structure of the evolution which is typically static. Specifically, self-adaptation of crossover and mutation operators in GNP aiming to change the rate of them by evolution is studied in this paper. Our method based on GNP can measure the significance of the association via the chi-squared test and obtain a sufficient number of important association rules. Extracted association rules are stored in a pool all together through generations and reflected in three genetic operators as acquired information.

Genetic Network Programming with Acquisition Mechanisms of Association Rules

2006 ◽  
Vol 10 (1) ◽  
pp. 102-111 ◽  
Author(s):  
Kaoru Shimada ◽  
◽  
Kotaro Hirasawa ◽  
Jinglu Hu
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Genetic Network ◽  
Network Programming ◽  
Genetic Operators ◽  
Rule Mining ◽  
Rule Acquisition ◽  
Chi Squared ◽  
Genetic Network Programming

A method of association rule mining using Genetic Network Programming (GNP) is proposed to improve the performance of association rule extraction. The proposed mechanisms can calculate measurements of association rules directly using GNP, and measure the significance of the association via the chi-squared test. Users can define the conditions of importance of association rules flexibly, which include the chi-squared value and the number of attributes in a rule. The proposed system evolves itself by an evolutionary method and obtains candidates of association rules by genetic operations. Extracted association rules are stored in a pool all together through generations and reflected in genetic operators as acquired information. Besides, our method can contain negation of attributes in association rules and suit association rule mining from dense databases. In this paper, we describe an extended algorithm capable of finding important association rules using GNP with sophisticated rule acquisition mechanisms and present some experimental results.

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
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