Improved Approach of Genetic Programming and Applications for Data Mining

2006 ◽  
pp. 816-819 ◽  
Author(s):  
Yongqiang Zhang ◽  
Huashan Chen
Keyword(s):  
Data Mining ◽  
Genetic Programming

A GRAMMAR-GUIDED GENETIC PROGRAMMING FRAMEWORK CONFIGURED FOR DATA MINING AND SOFTWARE TESTING

2006 ◽  
Vol 16 (02) ◽  
pp. 245-267 ◽  
Author(s):  
SILVIA REGINA VERGILIO ◽  
AURORA POZO
Keyword(s):  
Data Mining ◽  
Genetic Programming ◽  
Software Testing ◽  
Relational Databases ◽  
Programming Framework ◽  
Induction Technique

Genetic Programming (GP) is a powerful software induction technique that can be applied to solve a wide variety of problems. However, most researchers develop tailor-made GP tools for solving specific problems. These tools generally require significant modifications in their kernel to be adapted to other domains. In this paper, we explore the Grammar-Guided Genetic Programming (GGGP) approach as an alternative to overcome such limitation. We describe a GGGP based framework, named Chameleon, that can be easily configured to solve different problems. We explore the use of Chameleon in two domains, not usually addressed by works in the literature: in the task of mining relational databases and in the software testing activity. The presented results point out that the use of the grammar-guided approach helps us to obtain more generic GP frameworks and that they can contribute in the explored domains.

Predicting student failure at school using genetic programming and different data mining approaches with high dimensional and imbalanced data

2012 ◽  
Vol 38 (3) ◽  
pp. 315-330 ◽  
Author(s):  
Carlos Márquez-Vera ◽  
Alberto Cano ◽  
Cristóbal Romero ◽  
Sebastián Ventura
Keyword(s):  
Data Mining ◽  
Genetic Programming ◽  
Imbalanced Data ◽  
High Dimensional ◽  
Student Failure
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